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Remote Sensing
Volume 17
Issue 9
10.3390/rs17091527
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections

by
Gideon Asare Owusu
1,*,
Ashutosh Dumka
1,
Adu-Gyamfi Kojo
1,
Enoch Kwasi Asante
1,
Rishabh Jain
2,
Skylar Knickerbocker
3,
Neal Hawkins
3 and
Anuj Sharma
1
1
Department of Civil, Construction and Environmental Engineering (CCEE), Iowa State University, Ames, IA 50011-1066, USA
2
Department of Computer Science (COM S), Iowa State University, Ames, IA 50011-1066, USA
3
Institute for Transportation, Iowa State University of Science and Technology, Ames, IA 50011-1066, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(9), 1527; https://doi.org/10.3390/rs17091527
Submission received: 28 February 2025 / Revised: 18 April 2025 / Accepted: 23 April 2025 / Published: 25 April 2025
(This article belongs to the Special Issue Advancements in Autonomous UAVs for Infrastructure Inspection and Monitoring)
Versions Notes

Abstract

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.
Keywords: automated seat belt compliance detection; UAV-based monitoring; vehicle occupant safety; aerial video analysis; large language models automated seat belt compliance detection; UAV-based monitoring; vehicle occupant safety; aerial video analysis; large language models

Share and Cite

MDPI and ACS Style

Owusu, G.A.; Dumka, A.; Kojo, A.-G.; Asante, E.K.; Jain, R.; Knickerbocker, S.; Hawkins, N.; Sharma, A. Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections. Remote Sens. 2025, 17, 1527. https://doi.org/10.3390/rs17091527

AMA Style

Owusu GA, Dumka A, Kojo A-G, Asante EK, Jain R, Knickerbocker S, Hawkins N, Sharma A. Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections. Remote Sensing. 2025; 17(9):1527. https://doi.org/10.3390/rs17091527

Chicago/Turabian Style

Owusu, Gideon Asare, Ashutosh Dumka, Adu-Gyamfi Kojo, Enoch Kwasi Asante, Rishabh Jain, Skylar Knickerbocker, Neal Hawkins, and Anuj Sharma. 2025. "Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections" Remote Sensing 17, no. 9: 1527. https://doi.org/10.3390/rs17091527

APA Style

Owusu, G. A., Dumka, A., Kojo, A.-G., Asante, E. K., Jain, R., Knickerbocker, S., Hawkins, N., & Sharma, A. (2025). Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections. Remote Sensing, 17(9), 1527. https://doi.org/10.3390/rs17091527

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