Tool Recognition Method for Machine Tools Based on Improved YOLO v5
The object detection has the problems of small application range and low recognition precision.An im-proved Yolo v5 tool image recognition algorithm is proposed.Based on the idea of convolutional neural network,CBAM at-tention module is added to the feature extraction layer to extract image features more clearly,and the CARAFE sampling module is added to the feature fusion layer.The experimental results show that the improved algorithm can obviously im-prove the detection accuracy and speed of small targets,such as machine tool and so on,the average accuracy of the im-proved model is 96.8%,which is 14.96%higher than that of the YOLO v4 model and 2%higher than that of the YOLO v5 model.The method in this paper can be used to identify different cutting tools and provide a new algorithm support for the identification of mechanical parts in industrial manufacturing.