WPC-SS: multi-label wear particle classification based on semantic segmentation

In this paper, a novel wear particle online images multi-label classification based on semantic segmentation (WPC-SS) is proposed. In this model, both semantic labels and class labels are applied to guide network training, which make the regions with wear particles attain more attention during the process of network training. It solves the problem that it is difficult to classify the small wear particles when they are compared with the background in the online image. In addition, chain channel attention and class attention unit are added to optimize the network to improve the recognition accuracy. The important channels of the network are monitored by chain channel attention, so that the extracted features can be better prepared for the subsequent classification work. Class attention unit can refine the segmentation results and further optimize the classification results. Comparison experiments are executed with the standard image classification method (multi-CNN). The results of experiments show that WPC-SS model surpasses the standard image classification methods in solving the problem of multi-label classification of online wear particle images.