Objective:To solve the problem of unsatisfactory detection effects and high missed detection rate when the existing object detection algorithm performing small object detection,an improved YOLOv5s detection algorithm was proposed to improve the effect of small object detection.Methods:On the basis of the original model,the BottleneckCSP module was introduced and the large-scale feature fusion structure was added to improve the model's ability to capture small object features,and the SE attention mechanism was integrated into the network structure,so that the network self-learning paid more attention to the small object feature channel and enhanced the detection effect of the network model on small objects.Results:Comparing with the existing algorithms,the training verification on the same self-made small object detection dataset could effectively improve the mAP value and training convergence speed of the YOLOv5s object detection framework,expanded the small object detection range(from 0.002 5-0.010 0 to 0.000 8-0.001 4 of the original algorithm),and improved the small object detection performance(the average detection rate was increased by 46％).Conclusion:The improved algorithm could effectively improve the ability of small object detection.
维普
万方数据