Defect detection of energized grid based on Machine Vision

In recent years, automatic detection methods based on Machine Vision have been widely used in industrial field which not only save manpower effectively but also improve product quality and detection efficiency. The energized grids produced in the industry are large in size and have a great number of connection points. Due to the poor reliability and low efficiency of traditional manual detection, a method for defect detection of energized grids based on Machine Vision is proposed. At first, the images obtained by the network camera are corrected and cropped. Then enhance the image features and extract the region of interest by image processing technology. On this basis, detect defects through the Rhombus-based Point Correction (RSPC) algorithm and the Points Rearrangement (PR) algorithm. Experimental results show that the solution proposed can detect the defects of energized grid with an accuracy rate of about 97.5% and meet the needs of industrial production.