AI-driven real-time detection of motorcycle traffic violations using YOLOv8 and IoT-based analytics
Year: 2026
Type: article
Abstract: Motorcycle-related traffic violations, including helmetless riding and multiple riders, present significant challenges for urban traffic management, especially under conditions of occlusion and high traffic density. This research introduces a hybrid, real-time traffic violation detection framework that improves detection reliability by combining deep learning-based object detection with a secondary verification mechanism. The framework utilizes the lightweight YOLOv8n model to detect motorcycles, riders, and helmets, and incorporates a Haar Cascade-based module to verify rider count and minimize false detections in congested environments. Evaluation is conducted using real-world traffic video data collected across diverse environmental conditions. Performance is measured using standard metrics such as precision, recall, F1-score, and mean average precision ([email protected]). Experimental results show that the proposed method achieves a detection accuracy of 93.4% and a mAP of 94.8%, while sustaining real-time processing at approximately 38 frames per second. The verification stage enhances detection robustness, particularly in cases of occlusion and overlapping riders. These findings suggest that the hybrid framework offers a reliable and computationally efficient solution for real-time traffic violation detection, with strong applicability in intelligent transportation systems and smart city initiatives.
Source: Scientific Reports
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7160837943
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