Improved Target Detection Algorithm for Lightweight Parts of YOLOv5s
In order to solve the problem of difficulty in identifying multi-scale parts with disordered and dense placement,this paper proposes a target detection algorithm for lightweight parts based on improved YOLOv5s. The CA attention mechanism is added to the C3 module of the backbone network to improve the target feature extraction ability. The Efficient-RepGFPN structure of damo-yolo was introduced to replace the original Neck layer to reduce the complexity of the model. The SimAM attention mechanism is intro-duced to improve the representation ability of convolutional networks. In order to speed up the computation speed and reduce the computational cost,the lightweight convolution GSConv is used to replace the stand-ard convolution in the Neck structure. FocalEIOU was used to replace the CIOU in the YOLOv5 algorithm to optimize the recognition performance of the model. The experimental results show that on the self-made parts dataset,the mAP@0.5 of the improved algorithm reaches 99.4％,the detection speed is only 5.7 ms,the FPS reaches 175 frames/s,and the amount of calculation and parameters are greatly reduced,the model size is only 32％ of the original,easy to deploy on the mobile terminal,and is better than the original YOLOv5s in terms of part detection accuracy and detection speed,so as to meet the accurate identification of parts under visual guidance.
万方数据
维普
