Traffic Density Detection on Public CCTV of Malang City Government Using YOLOv8

Abstract

The increasing number of motor vehicles in Malang City has led to a rise in traffic volume, resulting in a higher risk of congestion, especially on major roads such as Jalan Ahmad Yani. To address this issue, an automated system capable of detecting and monitoring traffic density is needed. This study aims to develop a traffic density detection system using the YOLOv8 object detection model with video data from public CCTV operated by the Government of Malang City. The methodology includes video data collection, data preprocessing, model training using YOLOv8, model testing, and traffic density calculation based on the number of detected vehicles. The model was evaluated during training using precision and recall metrics, resulting in a precision of 89.4% and a recall of 85.2%. Model testing was conducted on test videos by calculating accuracy based on a comparison between the number of vehicles detected by the system and the manual vehicle count, resulting in an average accuracy of 92.8%. These results indicate that the model is capable of accurately detecting vehicles in real-world conditions. This system can serve as a foundation for developing automated traffic monitoring systems that utilize visual data from CCTV.