Abstract: The number, position distribution and distribution density of the diamond particles fixed on the diamond wire are important parameters to measure the surface quality of the diamond wire, and also important indicators to measure the cutting ability of the diamond wire. Aiming at the problems of small, dense and adhesive diamond particles, such as difficult to extract their features and low accuracy, a method of surface quality detection based on improved YOLOv5 diamond wire is proposed by using deep learning technology. First, in the image processing stage, the threshold segmentation technology is used to preliminarily divide large and small particles; Secondly, in the backbone network part, the CA (coordinated attention) attention module is added to obtain high-quality single-particle boundary features in the adhesive particles; The C2 (CA+CBL) module is designed again to preserve the semantic information between different layers by feature fusion, thus improving the detection accuracy of dense small objects; Finally, replace CSP2_X with a convolution structure, reduce the calculation amount, and reduce the receptive field of the output characteristic map of different scales to avoid the virtualization of particle characteristics, thus affecting the particle detection accuracy. Experiments show that the improved network model can effectively identify the images of diamond particles with different shapes, sizes, adhesion and density, the average accuracy (AP) of the particles, and the large particles are 83.80%, and 90.70%, respectively, and the mean average precision (mAP) is 87.20%.

Key words: diamond wire, YOLOv5, attention mechanism, small object detection, threshold segmentation