Research on surface defects detection method and system in manufacturing processes based on the fusion of multi-scale features and semantic segmentation for intelligent manufacturing

Abstract

Multi-scale defect features, blurred edges and inability to locate geometric features have been the three key factors limiting the detection of surface defects on quality control system in the industrial manufacturing process. In this study, a method based on the fusion of multi-scale features and pixel-level semantic segmentation is proposed for the detection of surface defects. The proposed method firstly fuses multi-level feature maps to balance the expressiveness of multi-scale features, then adds a boundary refinement module to enhance the accurate inference of edge fine-grained, and finally adopts an en-decoder architecture to locate geometric features at the pixel-level for each type of defects, realizing intelligent detection of geometric features of end-to-end multi-scale defects on the surface of parts. We conduct experiments using the collected parts datasets to evaluate the effectiveness of our framework. The experimental results show that the proposed model achieves MIoU of 80.1%, the recognition accuracy reaches more than 95 %, and a detection rate of up to 29.64 FPS, demonstrating the advancement and effectiveness of the proposed method with less misclassification and superior generalization performance and has progress and effectiveness in detecting surface defects of multi-scale features. It provides a research idea for the subsequent realization of surface quality inspection in the manufacturing process system.