Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections
Transportation agencies often rely on manual surveys to monitor seat belt compliance; however, these methods are limited by surveyor fatigue, reduced visibility due to tinted windows or low lighting, and restricted geographic coverage, making manual surveys prone to errors and unrepresentative of th...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1527 |
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| author | Gideon Asare Owusu Ashutosh Dumka Adu-Gyamfi Kojo Enoch Kwasi Asante Rishabh Jain Skylar Knickerbocker Neal Hawkins Anuj Sharma |
| author_facet | Gideon Asare Owusu Ashutosh Dumka Adu-Gyamfi Kojo Enoch Kwasi Asante Rishabh Jain Skylar Knickerbocker Neal Hawkins Anuj Sharma |
| author_sort | Gideon Asare Owusu |
| collection | DOAJ |
| description | Transportation agencies often rely on manual surveys to monitor seat belt compliance; however, these methods are limited by surveyor fatigue, reduced visibility due to tinted windows or low lighting, and restricted geographic coverage, making manual surveys prone to errors and unrepresentative of the broader driving population. This paper presents an automated seat belt detection system leveraging the YOLO11 neural network on video footage captured by a tethered uncrewed aerial vehicle (UAV). The objectives are to (1) develop a robust system for detecting seat belt use at stop-controlled intersections, (2) evaluate factors affecting detection accuracy, and (3) demonstrate the potential of UAV-based compliance monitoring. The model was tested in real-world scenarios at a single-lane and a complex multi-lane stop-controlled intersection in Iowa. Three studies examined key factors influencing detection accuracy: (i) seat belt–shirt color contrast, (ii) sunlight direction, and (iii) vehicle type. System performance was compared against manual video review and large language model (LLM)-assisted analysis, with assessments focused on accuracy, resource requirements, and computational efficiency. The model achieved a mean average precision (mAP) of 0.902, maintained high accuracy across the three studies, and outperformed manual methods in reliability and efficiency while offering a scalable, cost-effective alternative to LLM-based solutions. |
| format | Article |
| id | doaj-art-9122d9a558754a5981ff3b8a7ec9fe2e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9122d9a558754a5981ff3b8a7ec9fe2e2025-08-20T02:31:08ZengMDPI AGRemote Sensing2072-42922025-04-01179152710.3390/rs17091527Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled IntersectionsGideon Asare Owusu0Ashutosh Dumka1Adu-Gyamfi Kojo2Enoch Kwasi Asante3Rishabh Jain4Skylar Knickerbocker5Neal Hawkins6Anuj Sharma7Department of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USADepartment of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USADepartment of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USADepartment of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USADepartment of Computer Science (COM S), Iowa State University, Ames, IA 50011-1066, USAInstitute for Transportation, Iowa State University of Science and Technology, Ames, IA 50011-1066, USAInstitute for Transportation, Iowa State University of Science and Technology, Ames, IA 50011-1066, USADepartment of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USATransportation agencies often rely on manual surveys to monitor seat belt compliance; however, these methods are limited by surveyor fatigue, reduced visibility due to tinted windows or low lighting, and restricted geographic coverage, making manual surveys prone to errors and unrepresentative of the broader driving population. This paper presents an automated seat belt detection system leveraging the YOLO11 neural network on video footage captured by a tethered uncrewed aerial vehicle (UAV). The objectives are to (1) develop a robust system for detecting seat belt use at stop-controlled intersections, (2) evaluate factors affecting detection accuracy, and (3) demonstrate the potential of UAV-based compliance monitoring. The model was tested in real-world scenarios at a single-lane and a complex multi-lane stop-controlled intersection in Iowa. Three studies examined key factors influencing detection accuracy: (i) seat belt–shirt color contrast, (ii) sunlight direction, and (iii) vehicle type. System performance was compared against manual video review and large language model (LLM)-assisted analysis, with assessments focused on accuracy, resource requirements, and computational efficiency. The model achieved a mean average precision (mAP) of 0.902, maintained high accuracy across the three studies, and outperformed manual methods in reliability and efficiency while offering a scalable, cost-effective alternative to LLM-based solutions.https://www.mdpi.com/2072-4292/17/9/1527automated seat belt compliance detectionUAV-based monitoringvehicle occupant safetyaerial video analysislarge language models |
| spellingShingle | Gideon Asare Owusu Ashutosh Dumka Adu-Gyamfi Kojo Enoch Kwasi Asante Rishabh Jain Skylar Knickerbocker Neal Hawkins Anuj Sharma Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections Remote Sensing automated seat belt compliance detection UAV-based monitoring vehicle occupant safety aerial video analysis large language models |
| title | Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections |
| title_full | Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections |
| title_fullStr | Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections |
| title_full_unstemmed | Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections |
| title_short | Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections |
| title_sort | uncrewed aerial vehicle based automatic system for seat belt compliance detection at stop controlled intersections |
| topic | automated seat belt compliance detection UAV-based monitoring vehicle occupant safety aerial video analysis large language models |
| url | https://www.mdpi.com/2072-4292/17/9/1527 |
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