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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/9/1527 |
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| Summary: | 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. |
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| ISSN: | 2072-4292 |