Vehicle target detection in complex scenes based on YOLOv3 algorithm

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Ouyang, Lecheng^1 ; Wang, Huali^1

College of Communication Engineering, PLA Army Engineering University, Nanjing, Jiangsu

210007, China^1

关键词: Algorithm framework;    Binary classification problems;    Detection models;    Detection problems;    Detection speed;    Recognition accuracy;    Target detection algorithm;    Vehicle targets;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052018/pdf
DOI  :  10.1088/1757-899X/569/5/052018

来源: IOP

【 摘 要 】

In view of the low accuracy of traditional vehicle target detection methods in complex scenes, combined with the current hot development of deep learning, this paper applies the YOLOv3 algorithm framework to achieve vehicle target detection. By using PASCAL VOC2007 and VOC2012 data sets, images containing vehicle targets were screened out to constitute the VOC car data set, and the target detection problem was converted into a binary classification problem. Then loading the pre-trained YOLOv3 model weight, and training the vehicle target detection model weight based on YOLOv3 algorithm, which is used to detect the test samples. Experimental results show that this method has advantages over the traditional target detection algorithms in recognition accuracy and detection speed.