Non-Contact Overloading Detection System for Public Utility Buses Using Yolov8 Algorithm
TL;DR
Findings indicate that the proposed system is feasible for real-time overloading monitoring, though performance remains sensitive to occlusion, lighting, crowd density, and network stability.
Abstract
Overloading in public utility buses remains a persistent transportation safety and regulatory problem, particularly in urban environments, where monitoring is often conducted through manual inspection. This study developed and evaluated a non-contact overloading detection system for public utility buses using the YOLOv8 object detection algorithm, Raspberry Pi 4, camera module, GPS, and real-time alert transmission. The study employed a descriptive-developmental quantitative design and Agile software development methodology. Fifty respondents participated in the evaluation, consisting of 44 passengers and 6 authorities. System performance was assessed through controlled trials, ISO/IEC 25010-based survey questionnaires, and object detection metrics, including precision, recall, and mean average precision (mAP). Test trials showed detection accuracies ranging from 67% to 82%, with one case of over-detection. Real-time processing achieved an average speed of 0.04 to 0.09 seconds per frame. Survey-based evaluation indicated high acceptance across hardware detection (overall mean = 4.40), alert system performance (overall mean = 4.41), and real-world system performance (overall mean = 4.37). ISO/IEC 25010 evaluation also showed strong results in usability (4.46), security (4.44), maintainability (4.44), reliability (4.37), efficiency (4.37), and functionality (4.19). YOLOv8 performance metrics reported precision = 0.94, recall = 0.91, mAP50 = 0.95, and mAP50-95 = 0.88. Findings indicate that the proposed system is feasible for real-time overloading monitoring, though performance remains sensitive to occlusion, lighting, crowd density, and network stability.