Objective:To address the problems of low detection accuracy and easy missed detection of dense dynamic targets , this paper proposed an improved detection algorithm based on YOLOv5.Methods:In order to improve the training speed of the deep learning model ,the QARepNeXt structure was used in the backbone network of YOLOv5.At the same time ,in order to improve the problem of poor detection results under occlusion ,the S2-MLPv2 attention mechanism was introduced.In addition , in order to improve the convergence speed ,the boundary regression loss function Wise-IoU with the dynamic focusing mechanism replaced the original loss function.Results:Taking the dense crowd as an example ,it was verified that the improved algorithm had higher accuracy and lower miss detection rate than the existing algorithm , while ensuring the real-time performance of the original algorithm.Conclusion:By replacing the network structure ,introducing the attention mechanism and updating the loss function ,the accuracy of the algorithm could be effectively improved and the missed detection rate could be reduced.
关键词
动态目标检测/YOLOv5/深度学习/注意力机制/损失函数
Key words
Dense pedestrian detection/YOLOv5/Deep learning/Attention mechanism/Loss function
维普
万方数据