{"id":"AIBD-1200","title":"The FAA's Airport Technology R&D Branch reports that its drone and FastFlow deep-learning workflow detected 96% of advisory-circular FOD items","summary":"The FAA's Airport Technology R&D Branch reports that its drone-based FOD detection workflow with the FastFlow deep-learning algorithm detected 173 of 180 (96%) test items at two New Jersey airports.","date_observed":"2024-09-01","date_basis":"source publication","date_precision":"month","benefit_status":"mixed","source_collection":null,"source_entry":null,"account":"The FAA's Airport Technology Research and Development Branch, with contractors Woolpert and Neya Systems, built a proof-of-concept workflow to detect foreign object debris on airport surfaces. A small unmanned aircraft photographs the pavement, and a trained deep-learning anomaly detector, FastFlow, flags debris in the images. The team tested the workflow at Cape May County Airport in three stages and then validated it on a runway at Atlantic City International Airport. It placed the debris items that FAA Advisory Circular 150/5220-24 specifies. In its report of September 2024, the branch states that 173 of 180 items (96%) were detected across initial and validation testing, above the circular's 90% requirement. Lug nuts, deep sockets and distorted metal strips were missed most often. The workflow met the circular's location accuracy, inspection frequency and surveillance area requirements. It fell short on false positives, which greatly exceeded the allowed one per day, on processing time, on a software interface for alerts and on low-light and bad-weather operation. The team tested its own workflow. No airport used it in operation, and no debris strike was prevented.","category":"aggregate result","unexpected":false,"purpose":"The FAA's Airport Technology Research and Development Branch built the sUAS-based FOD detection workflow to test whether commercial drones and machine-learning algorithms can detect foreign object debris on airport surfaces to the standard of Advisory Circular 150/5220-24.","system":"sUAS-based FOD detection workflow using the FastFlow ML deep learning algorithm (an OCR-GAN variant was also tested)","model_version":null,"developer":"FAA Airport Technology Research and Development Branch research team, with Woolpert, Inc. and Neya Systems (Applied Research Associates)","deployer":"None in operation (tests by the FAA research team at Cape May County Airport and Atlantic City International Airport)","location":"Cape May County Airport and Atlantic City International Airport, New Jersey, United States","domain":"Aviation safety / airport FOD detection","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.1.b.1","4.A.1.b.2"],"taxonomy_note":"The report measures a detection rate in tests, and the aim is airport safety, since debris on runways damages aircraft.","registry_ids":{},"ratings":[{"group":"organizational","depth":null,"reason":"The workflow was a proof of concept tested with placed items, and no airport used it to find debris in operation."}],"ai_role":{"status":"unassessed","statement":"The report measures what the drone-plus-FastFlow workflow detected of placed test items against the advisory circular's requirements. It does not compare detection with and without the algorithm or against human inspection, and the research team built and tested its own workflow."},"evidence":[{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"The sUAS-based FOD detection workflow, which used the FastFlow ML deep learning algorithm, was capable of meeting some of the AC 150/5220-24 requirements, including achieving a 96% detection rate for FOD items specified in the AC."},{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024. Section 6.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"Of 180 FOD items, 173 (96%) were detected overall. The FOD items not detected included the lug nut (12 of 15 detected), the deep socket (14 of 15 detected), and the distorted metal strip (12 of 15 detected)."}],"replication":{"produced_by":["The team flew a commercial small unmanned aircraft over runways and trained the FastFlow deep-learning anomaly detector on the images.","Test items from the advisory circular's list were placed on the pavement at two airports.","Images taken from directly above at a 0.2-cm ground sampling distance detected more items than oblique images."],"must_hold":["Daylight and fair weather, since the workflow did not cover low light or inclement weather.","Someone must sort the false positives, which greatly exceeded the circular's allowed rate.","Processing time must be acceptable, and the report says that it still needs reduction."],"echoes":[]},"paired_incident":null,"headline":"Drone plus *FastFlow* finds 96% of test runway debris for the FAA","metric":"173 of 180 (96%) test items","comparator":{"against":"The detection requirement of FAA Advisory Circular 150/5220-24","before":"90% of the specified group of objects","after":"96% (173 of 180 items) detected overall","basis":"measured","quote":"Of 180 FOD items, 173 (96%) were detected overall.","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf"},"later":null,"learns_from_data":{"value":"shown","note":"The report says that the team developed and trained a deep-learning network on images collected at the airports.","quote":"The research team developed and trained an AI/ML deep learning network","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf"},"benefit_dimensions":["functional"],"benefit_categories":["physical_health"],"scsp_example":"Hazard reduction","size":{"reach":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The team tested the workflow with placed debris at two airports, and no airport used it in operation."},"duration":{"band":null,"fact":null,"quote":null,"url":null,"reason":"No airport used the workflow, so no lasting benefit is documented."},"distribution":{"band":null,"fact":null,"quote":null,"url":null,"reason":"No realized benefit is documented, and the report compares no groups."},"frequency":{"band":null,"fact":null,"quote":null,"url":null,"reason":"The workflow was only tested and never run in operation, so no frequency of benefit can be stated."}},"value":null,"developer_country":"United States","deployer_country":"United States","unrated_because":"The branch tested a proof-of-concept workflow with placed debris items, so no realized outcome for any airport is reported.","brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the report of the FAA Airport Technology Research and Development Branch.","source":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","editor":"AIBD"},{"date":"2026-11-01","change":"Placed the benefit under its broad categories and recorded its reach, duration, distribution and frequency from its sources.","source":null,"editor":"AIBD"}],"confidence":"claimed","shade":"low","claim_status":"The makers' claim","claim_basis":"Sources: the makers' report. The cited evidence includes no independent source and no finding by an external body.","case_jurisdictions":["United States"],"profile":{"case_id":"AIBD-1200","catalog_version":"1.0.0","source_fingerprint":"5583a81cb2a3be5ed89666f1357d4d37be3dd4f205e6c1c6037af9cff10d07e1","collection_refs":[{"collection_id":"aibd","version":"2026-10-31"}],"source_ids":["AIBD-1200-source-0001","AIBD-1200-source-0002"],"families":{"strategic assessments":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"evaluations":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"monetary observations":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"institutional decisions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]}},"fields":{"actors[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"developer":"FAA Airport Technology Research and Development Branch research team, with Woolpert, Inc. and Neya Systems (Applied Research Associates)","deployer":"None in operation (tests by the FAA research team at Cape May County Airport and Atlantic City International Airport)"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["developer","deployer"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"actors[].ownership_control":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"ai_contribution":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"ai_relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"category":"aggregate result","ai_role":{"status":"unassessed","statement":"The report measures what the drone-plus-FastFlow workflow detected of placed test items against the advisory circular's requirements. It does not compare detection with and without the algorithm or against human inspection, and the research team built and tested its own workflow."}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ai_role","category"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].alternatives":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].assessor_date":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].depth":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].display_confidence":{"state":"value_present","value":{"kind":"structured","data":{"confidence":"low","evidence_word":"claimed","claim_status":"The makers' claim","basis":"Sources: the makers' report. The cited evidence includes no independent source and no finding by an external body.","interpretation":"Existing display rule preserved; the richer outcome-specific confidence question still needs review."}},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].rationale":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","reason":"The workflow was a proof of concept tested with placed items, and no airport used it to find debris in operation."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].rubric_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].status_basis":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].target":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","reason":"The workflow was a proof of concept tested with placed items, and no airport used it to find debris in operation."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"availability":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"brief":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"case_kind":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"case_links[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].evidence_links":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].evidence_strength":{"state":"value_present","value":{"kind":"structured","data":{"answer":"The makers' claim. Sources: the makers' report. The cited evidence includes no independent source and no finding by an external body.","evidence_word":"claimed","claim_status":"The makers' claim"}},"provenance":{"kind":"source_review","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":["AIBD-1200-source-0001","AIBD-1200-source-0002"],"reviewed_on":"2026-10-29"},"review_state":"reviewed","coverage":"complete","display":{"answer":"The makers' claim. Sources: the makers' report. The cited evidence includes no independent source and no finding by an external body.","inferred":false}},"claims[].missingness":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].proposition":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"cross_border_flows[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"decisions[].action_outcome":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].grounds":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].institution_task":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].success_criterion":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"deployment_stage":{"state":"value_present","value":{"kind":"structured","data":{"classification":"laboratory test","answer":"The system was tested in a laboratory. The branch tested a proof-of-concept workflow with placed debris items on airport runways, with no operational use.","scope":"The period and activity described in the case."}},"provenance":{"kind":"source_review","basis":"Editorial assignment.","record_fields":["account","evidence"],"source_refs":["AIBD-1200-source-0001"],"reviewed_on":"2026-10-29"},"review_state":"reviewed","coverage":"complete","display":{"answer":"The system was tested in a laboratory. The branch tested a proof-of-concept workflow with placed debris items on airport runways, with no operational use.","inferred":false}},"economics[].amount":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].costs[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].counterfactual":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].currency_price_basis":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].gross_net":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].measure_type":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].period_unit":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].realization":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].recipient_payer":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].return_payback":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].valuation_method":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"evaluations[].ai_uplift":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].comparator":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].design":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].effect":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].exposure":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].id":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].measurement_method":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].metric":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].reproduction_materials":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].setting_sample":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].uncertainty_limitations":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"external_references[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"follow_up[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"human_oversight":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"id":{"state":"value_present","value":{"kind":"record_fields","fields":{"id":"AIBD-1200"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["id"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"infrastructure_dependencies[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].beneficiaries":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].beneficiary_count":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].benefit_type":{"state":"value_present","value":{"kind":"record_fields","fields":{"benefit_type":"Improved Task Accuracy","taxonomy_note":"The report measures a detection rate in tests, and the aim is airport safety, since debris on runways damages aircraft."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["benefit_type","taxonomy_note"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].claim":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"The FAA's Airport Technology Research and Development Branch, with contractors Woolpert and Neya Systems, built a proof-of-concept workflow to detect foreign object debris on airport surfaces. A small unmanned aircraft photographs the pavement, and a trained deep-learning anomaly detector, FastFlow, flags debris in the images. The team tested the workflow at Cape May County Airport in three stages and then validated it on a runway at Atlantic City International Airport. It placed the debris items that FAA Advisory Circular 150/5220-24 specifies. In its report of September 2024, the branch states that 173 of 180 items (96%) were detected across initial and validation testing, above the circular's 90% requirement. Lug nuts, deep sockets and distorted metal strips were missed most often. The workflow met the circular's location accuracy, inspection frequency and surveillance area requirements. It fell short on false positives, which greatly exceeded the allowed one per day, on processing time, on a software interface for alerts and on low-light and bad-weather operation. The team tested its own workflow. No airport used it in operation, and no debris strike was prevented.","benefit_type":"Improved Task Accuracy"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","benefit_type"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].distribution":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].duration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].id":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].stakeholder_group":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","reason":"The workflow was a proof of concept tested with placed items, and no airport used it to find debris in operation."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].status":{"state":"value_present","value":{"kind":"record_fields","fields":{"benefit_status":"mixed"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["benefit_status"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].tradeoffs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].unexpected":{"state":"value_present","value":{"kind":"record_fields","fields":{"unexpected":false,"purpose":"The FAA's Airport Technology Research and Development Branch built the sUAS-based FOD detection workflow to test whether commercial drones and machine-learning algorithms can detect foreign object debris on airport surfaces to the standard of Advisory Circular 150/5220-24."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["unexpected","purpose"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"places[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"location":"Cape May County Airport and Atlantic City International Airport, New Jersey, United States","developer_country":"United States","deployer_country":"United States"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["location","developer_country","deployer_country"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"purpose":{"state":"value_present","value":{"kind":"record_fields","fields":{"purpose":"The FAA's Airport Technology Research and Development Branch built the sUAS-based FOD detection workflow to test whether commercial drones and machine-learning algorithms can detect foreign object debris on airport surfaces to the standard of Advisory Circular 150/5220-24."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["purpose"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"record_dates":{"state":"value_present","value":{"kind":"record_fields","fields":{"date_observed":"2024-09-01","date_basis":"source publication","date_precision":"month","revisions":[{"date":"2026-11-01","change":"Added the record from the report of the FAA Airport Technology Research and Development Branch.","source":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","editor":"AIBD"},{"date":"2026-11-01","change":"Placed the benefit under its broad categories and recorded its reach, duration, distribution and frequency from its sources.","editor":"AIBD"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["date_observed","date_basis","date_precision","revisions"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"replication.attempts[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"replication.conditions":{"state":"value_present","value":{"kind":"record_fields","fields":{"replication":{"produced_by":["The team flew a commercial small unmanned aircraft over runways and trained the FastFlow deep-learning anomaly detector on the images.","Test items from the advisory circular's list were placed on the pavement at two airports.","Images taken from directly above at a 0.2-cm ground sampling distance detected more items than oblique images."],"must_hold":["Daylight and fair weather, since the workflow did not cover low light or inclement weather.","Someone must sort the false positives, which greatly exceeded the circular's allowed rate.","Processing time must be acceptable, and the report says that it still needs reduction."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"revision_history[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"revisions":[{"date":"2026-11-01","change":"Added the record from the report of the FAA Airport Technology Research and Development Branch.","source":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","editor":"AIBD"},{"date":"2026-11-01","change":"Placed the benefit under its broad categories and recorded its reach, duration, distribution and frequency from its sources.","editor":"AIBD"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["revisions"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sector":{"state":"value_present","value":{"kind":"record_fields","fields":{"domain":"Aviation safety / airport FOD detection"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["domain"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].access_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].citation_locator":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"The sUAS-based FOD detection workflow, which used the FastFlow ML deep learning algorithm, was capable of meeting some of the AC 150/5220-24 requirements, including achieving a 96% detection rate for FOD items specified in the AC."},{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024. Section 6.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"Of 180 FOD items, 173 (96%) were detected overall. The FOD items not detected included the lug nut (12 of 15 detected), the deep socket (14 of 15 detected), and the distorted metal strip (12 of 15 detected)."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].id":{"state":"value_present","value":{"kind":"identifiers","identifiers":["AIBD-1200-source-0001","AIBD-1200-source-0002"],"role":"Existing evidence items, in their original order"},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"sources[].language":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"The sUAS-based FOD detection workflow, which used the FastFlow ML deep learning algorithm, was capable of meeting some of the AC 150/5220-24 requirements, including achieving a 96% detection rate for FOD items specified in the AC."},{"kind":"report","citation":"Shaffer, L., Hall, D., Menezes, S., Rybski, P. and Brenner, R. (2024). Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces. DOT/FAA/TC-24/29, Federal Aviation Administration William J. Hughes Technical Center, Airport Technology Research and Development Branch, September 2024. Section 6.","title":"Small Unmanned Aircraft-Based Foreign Object Debris Detection on Airport Surfaces (DOT/FAA/TC-24/29)","url":"https://rosap.ntl.bts.gov/view/dot/77824/dot_77824_DS1.pdf","date":"2024-09-01","written_by":"makers","quote":"Of 180 FOD items, 173 (96%) were detected overall. The FOD items not detected included the lug nut (12 of 15 detected), the deep socket (14 of 15 detected), and the distorted metal strip (12 of 15 detected)."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"The FAA's Airport Technology R&D Branch reports that its drone-based FOD detection workflow with the FastFlow deep-learning algorithm detected 173 of 180 (96%) test items at two New Jersey airports.","account":"The FAA's Airport Technology Research and Development Branch, with contractors Woolpert and Neya Systems, built a proof-of-concept workflow to detect foreign object debris on airport surfaces. A small unmanned aircraft photographs the pavement, and a trained deep-learning anomaly detector, FastFlow, flags debris in the images. The team tested the workflow at Cape May County Airport in three stages and then validated it on a runway at Atlantic City International Airport. It placed the debris items that FAA Advisory Circular 150/5220-24 specifies. In its report of September 2024, the branch states that 173 of 180 items (96%) were detected across initial and validation testing, above the circular's 90% requirement. Lug nuts, deep sockets and distorted metal strips were missed most often. The workflow met the circular's location accuracy, inspection frequency and surveillance area requirements. It fell short on false positives, which greatly exceeded the allowed one per day, on processing time, on a software interface for alerts and on low-light and bad-weather operation. The team tested its own workflow. No airport used it in operation, and no debris strike was prevented."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"sUAS-based FOD detection workflow using the FastFlow ML deep learning algorithm (an OCR-GAN variant was also tested)"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"The FAA's Airport Technology R&D Branch reports that its drone and FastFlow deep-learning workflow detected 96% of advisory-circular FOD items","headline":"Drone plus *FastFlow* finds 96% of test runway debris for the FAA"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["7b76ff77f253a862a95766c49f2653d086cd5fb4136a46b36f7b7b163a5ff196"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"The FAA's Airport Technology Research and Development Branch, with contractors Woolpert and Neya Systems, built a proof-of-concept workflow to detect foreign object debris on airport surfaces. A small unmanned aircraft photographs the pavement, and a trained deep-learning anomaly detector, FastFlow, flags debris in the images. The team tested the workflow at Cape May County Airport in three stages and then validated it on a runway at Atlantic City International Airport. It placed the debris items that FAA Advisory Circular 150/5220-24 specifies. In its report of September 2024, the branch states that 173 of 180 items (96%) were detected across initial and validation testing, above the circular's 90% requirement. Lug nuts, deep sockets and distorted metal strips were missed most often. The workflow met the circular's location accuracy, inspection frequency and surveillance area requirements. It fell short on false positives, which greatly exceeded the allowed one per day, on processing time, on a software interface for alerts and on low-light and bad-weather operation. The team tested its own workflow. No airport used it in operation, and no debris strike was prevented.","replication":{"produced_by":["The team flew a commercial small unmanned aircraft over runways and trained the FastFlow deep-learning anomaly detector on the images.","Test items from the advisory circular's list were placed on the pavement at two airports.","Images taken from directly above at a 0.2-cm ground sampling distance detected more items than oblique images."],"must_hold":["Daylight and fair weather, since the workflow did not cover low light or inclement weather.","Someone must sort the false positives, which greatly exceeded the circular's allowed rate.","Processing time must be acceptable, and the report says that it still needs reduction."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":27,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":25,"review_not_assessed":64,"not_assessed":0,"complete":false,"by_state":{"value_present":27,"not_reported":0,"unresolved":64,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"434b252c4bac9dc1f3f81c7e99a3e6750e419b548fcd1ba21bc9be00b1c066cb"}