<![CDATA[Traffic jams have become a serious problem in many countries, including Indonesia. Traffic management is a challenging task, especially in developing countries. One of the causes of traffic jams is long lines of vehicles at intersections because the traffic light signals are on at the same time without paying attention to the number of vehicles queuing. The computer vision approach can be applied as a solution by minimizing waiting time for drivers by adjusting the length of time the green light signal is on based on the number of vehicles in each lane. To carry out this approach, a vehicle detection system was built using the YOLOv8 (You Only Look Once 8th version) algorithm, which operates in real-time. The data used comes from CCTV installed at every traffic light at intersection four. The YOLOv8 algorithm shows high accuracy and efficiency in object detection in various studies. To find out to what extent the YOLO model that has been built can predict the data correctly, it is necessary to evaluate the model by calculating accuracy, recall and mAP50 score. This research aims to build an intelligent traffic light management system using YOLOv8, based on computer vision and the OpenCV library. The results obtained from this system calculation are an accuracy value of 0.689, a recall value of 0.578, and an mAP50 score of 0.65.]]>
Copyrights © 2024
