Abstract:In order to solve the problem of low detection accuracy and slow convergence speed in the process of personnel detection, a YOLOv5 personnel detection method based on improvement is proposed. Firstly, the attention mechanism (CBAM) is added to the backbone network and the feature enhancement network (Neck) to solve the problem of insufficient feature extraction ability of Backbone and strengthen the ability of Neck feature fusion; Then, EIOU Loss was added to solve the problem of calculating the difference value of width and height instead of aspect ratio, and at the same time introduced Focal Loss to solve the problem of difficult sample imbalance, EIOU Loss not only accelerated the convergence speed of the model during the test, but also improved the accuracy. The results show that in the homemade dataset and the open dataset CrowdHuman, the average accuracy is improved by 1.2% and 1.6%, respectively, and the FPS is increased by 11.91 frames and 6.44 frames per second, and the missed detection situation is also reduced. After the improved model, the real-time requirements meet the realistic requirements, it is easier to extract the characteristic information of the personnel, and the detection accuracy is improved.