AN APPROACH FOR VEHICLE TARGET DETECTION USING YOLO V3

Abstract

In recent years, autonomous driving has become the key development and transformation direction of the traditional automobile industry. In the complex driving environment, automatic driving requires extremely high accuracy and high speed of detection, otherwise, it is easy to lead to traffic accident tragedy. Given the low accuracy of traditional car target detection methods in complex scenes, combined with the current hot development of deep learning, this project applies the YOLO V3 algorithm framework to achieve car target detection. In this project, we will detect cars moving on the road using deep learning techniques. Both detection and tracking vehicles such as cars, trucks, motorcycles, etc are challenging problems, especially in complex real-world scenes that commonly involve multiple vehicles, complicated occlusions, and cluttered or even moving backgrounds. This project mainly focuses on car detection can locate cars even in complex street scenes and it can apply to other vehicles by including other datasets and their class names, but false positives have remained frequent. The approximate articulation of each car is detected using YOLO V3 pretrained detector network and untrained network and their average precision and confidence scores are compared among them. The vehicle dataset having cars is used here. Software Tools: MATLAB 2021a or Above Keywords — Object Detection, Convolutional Neural Network, Deep Learning, YOLO V3