YOLO-Based Vehicle Detection: Literature Review

Abstract

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.