A Mechanical Parts Image Segmentation Method Against Illumination for Industry

Abstract

Most of the current image edge detection methods rely on manually features to extract the edge, there are often false and missed detections when the image has adverse interference. The surface of mechanical parts is smooth, when taking photos in the industrial field, it is easy to have specular reflection and shadow at the same time, which will affect the edge detection results. In order to achieve excellent edge detection performance, we propose a semantic segmentation model based on encoder-decoder structure. It adopts joint learning strategy, using two decoders to process image decomposition task and segmentation task respectively, and sharing their parameters to eliminate the influence of illumination, so as to improve segmentation performance. In the training phase, the asymmetric convolution and BN fusion are combined to improve the detection efficiency. In addition, we built a gear part dataset for experimentation. The result shows that in the task of edge detection of mechanical parts affected by illumination, our method has better performance than classical method.

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

“…A major issue is the difficulty of obtaining good quality images due to the complex geometries of the parts or very reflective or shiny materials, and the lighting variations that occur in a normal industrial environment. Thus, creating a stable robust machine vision solution is always a complex task [15][16][17].…”