<![CDATA[Traffic congestion at urban intersections in Indonesia, particularly in Palembang, is exacerbated by the frequent violation of Yellow Box Junction (YBJ) regulations. This study develops an automated detection system for vehicle stopping duration violations in YBJ areas using the YOLOv8 deep learning algorithm, specifically the yolov8n.pt model, optimized with a 3-frame skip technique to enhance computational efficiency. The system is designed to identify vehicles remaining within a predefined Region of Interest (ROI) for more than 5 seconds. Testing conducted at Simpang Angkatan 45 recorded 168 violations compared to 149 violations from manual observation. The primary contribution of this research lies in the development of an automated traffic law enforcement solution tailored to Indonesia's heterogeneous traffic conditions, as well as the implementation of computational optimization techniques that enable near real-time operation on mid-range hardware without significantly compromising detection accuracy . Although a 12.75% detection variance occurred due to ID switching and occlusion factors, this study provides a foundation for more accountable and scalable intelligent surveillance systems in the future.]]>
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