Detection of surface defect on flexible printed circuit via guided box improvement in GA-Faster-RCNN network.

Abstract

Industrial defect detection is a critical aspect of production. Traditional industrial inspection algorithms often face challenges with low detection accuracy. In recent years, the adoption of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in the field of computer vision. Our research primarily focused on developing a defect detection algorithm for the surface of Flexible Printed Circuit (FPC) boards. To address the challenges of detecting small objects and objects with extreme aspect ratios in FPC defect detection for surface, we proposed a guided box improvement approach based on the GA-Faster-RCNN network. This approach involves refining bounding box predictions to enhance the precision and efficiency of defect detection in Faster-RCNN network. Through experiments, we verified that our designed GA-Faster-RCNN network achieved an impressive accuracy rate of 91.1%, representing an 8.5% improvement in detection accuracy compared to the baseline model.