RDIDI: Recognition of Defect Image Detection in Industry

Abstract

Electronic consumer products are closely related to life. With the increase of demand, various consumer electronic products have emerged as the times require. For some high-precision equipment, such as in all aspects of the chip manufacturing process, the requirements for PCB are relatively high. In the PCB manufacturing process, it is often due to various problems caused by improper operation of certain links in the process. Many defects may appear on the PCB, such as bubbles appearing when the film is not firmly attached to the ground during the film attachment process, and bubbles appear during the exposure process. Negative film scratches, excessive pressure during etching, unevenness during plating, etc. Printed board surface defects vary in size. This inconsistency of multi-scale features will cause the pooling operation of the network model to lose some fine-grained spatial features. In the field of object detection, in the past, the detection effect in the field of PCB defect detection was poor, and the natural defects were few and small, and faced some bottlenecks in engineering. Aiming at this problem, a PCB defect detection method based on RDIDet is proposed. The experimental results prove that the improved network has obvious performance advantages over the previous classic model, with an accuracy rate of 98.3%, and a better detection effect on PCB defects.