Part Detection Based on Coordinated Attention Mechanism Lightweight YOLOv4
In order to solve the slow response speed,high hardware resource occupation and other issues of automatic detection tasks under such complex conditions as stacked adhesions and debris interference,a part detection method based on lightweight YOLOv4 network is proposed.MobileNeXt is used to replace CSPDarkNet53 as backbone,and coordinated attention mechanism is added in each convolutional module to enhance the semantic expression ability of feature maps.A Fused-Sandglass module is proposed for plugging into shallow backbone,which can improve the inference speed of network.In the aspect of network training,the progressive training method and focal loss function are introduced to improve the training speed and effectively alleviate the imbalance between positive and negative samples.Experimental results show that the proposed method can maintain the accuracy similar to that of YOLOv4 network in 15 parts detection tasks,and the number of parameters is only 20％of it.The inference speed can reach 43.7 fps,which can meet the demand of actual production.
deep learningcoordinating attention mechanismpart detectionYOLOv4 networkMobileNeXt network
万方数据
维普
