Malaysia ranks third among ASEAN countries in terms of deaths due to accidents, with an alarming increase in the number of fatalities each year. Road conditions contribute significantly to near-miss incidents, while the inefficiency of installed CCTVs and the lack of monitoring system algorithms worsen the situation. The objective of this research is to address the issue of increasing accidents and fatalities on Malaysian roads. Specifically, the study aims to investigate the use of video technology and machine learning algorithms for the car detection and analysis of near-miss accidents. To achieve this goal, the researchers focused on Penang, where the MBPP has deployed 1841 CCTV cameras to monitor traffic and document near-miss accidents. The study utilised the YOLOv3, YOLOv4, and Faster RCNN algorithms for vehicle detection. Additionally, the study employed image processing techniques such as Bird’s Eye View and Social Distancing Monitoring to detect and analyse how near misses occur. Various video lengths (20s, 40s, 60s and 80s) were tested to compare the algorithms’ error detection percentage and test duration. The results indicate that Faster RCNN beats YOLOv3 and YOLOV4 in car detection with low error detection, whereas YOLOv3 and YOLOv4 outperform near-miss detection, while Faster RCNN does not perform it. Overall, this study demonstrates the potential of video technology and machine learning algorithms in near-miss accident detection and analysis. Transportation authorities can better understand the causes of accidents and take appropriate measures to improve road safety using these models. This research can be a foundation for further traffic safety and accident prevention studies.

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