Pendekatan YOLOv5 dalam Mendeteksi dan Mengklasifikasikan Obyek Lalu Lintas serta Pelanggaran Melawan Arah

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.