Slender defect segmentation network of workpiece surface based on deep learning

Abstract

As a key part of the workpiece production process, workpiece surface defect segmentation detection plays an important role in the quality assurance of the workpiece. Aiming at the problem of low accuracy of workpiece surface slender defect segmentation, we propose a slender defect segmentation network based on feature enhancement module (FEM) and feature fusion module (FFM). Firstly, the ConvNeXt is improved and used as the backbone network. Then, the FEM is designed to obtain rich defect features. Finally, the FFM is designed to expand the receptive field and fuse shallow features with deep features to enhance the features of slender defects. The proposed method improves the segmentation accuracy of slender defects. The mean pixel accuracy) of slender defect segmentation on the self-built diode glass shell dataset and public magnetic sheet dataset is 97.87% and 94.86%, respectively. The mean Intersection over Union of slender defect segmentation is 94.32% and 83.89%, respectively. The segmentation speed reaches 43FPS, and the parameter is 7.12 M, which balances the segmentation accuracy, speed and model size.