2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC)
Download PDF

Abstract

In order to find dangerous targets in the crowd in time and prevent dangerous events from happening, this paper proposes a crowd abnormal behavior detection method based on YOLOV4 improved algorithm. By deepening the depth of the backbone feature network, the network parameters are updated with the exponential moving average; then the Mosica is replaced with Mixup data enhancement function; finally improve YOLOV4's extraction feature pyramid (PAN) structure to realize the recognition. The average detection accuracy (MAP) result obtained by the original YOLOV4 algorithm training model is 76.43%, and MAP value obtained by the improved YOLOV4 model is 81.41%. Compared with the original algorithm, the detection accuracy rates of the three detection categories fire, smoke, and knife are increased by 4.64%, 7.61%, and 2.68%, respectively. The experimental results show that the crowd abnormal behavior detection system based on YOLOV4 improved algorithm accuracy.
Like what you’re reading?
Already a member?
Get this article FREE with a new membership!

Related Articles