Video Target Detection Based on Improved YOLOV3 Algorithm

• Published in: Jisuanji kexue yu tansuo Vol. 15; no. 1; pp. 163 - 172
• Main Author: SONG Yanyan, TAN Li, MA Zihao, REN Xueping
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.01.2021
• Subjects: deep learning; repulsion loss; target detection; video understanding; yolov3 algorithm
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2003008

Pedestrian detection in monitoring has complex backgrounds, multiple target scales and poses, and occlusion between people and surrounding objects. As a result, the YOLOV3 algorithm is inaccurate in detecting some targets, which may result in false detection, missed detection, or repeated detection. Therefore, on the basis of YOLOV3's network, using the residual structure idea, the shallow and deep features are upsampled and fused to obtain 104×104 scale detection layers. And the size of the bounding box clustered by the K-means algorithm is applied to the network layer of each scale, which increases the sensitivity of the network to multi-scale and multi-pose targets and improves the detection effect. At the same time, the YOLOV3 loss function is updated using the repulsion loss of the prediction frame to other surrounding targets, so that the prediction frame is closer to the correct target, away from the wrong target. In addition, the false detection rate of the model is reduced, so as to improve the detec