P‐47: Exploration and Application of Unknown Category Defect Detection Methods for Display Panels

Defect detection is crucial in the panel industry, especially for unknown defects that occur during the production process. In order to solve this problem, we propose a novel feature fusion method, the Foreground‐background Same‐scale Difference Attention (FBSSDA) fusion module. Improvements are made to YOLOv5, and the FBSSDA‐YOLOv5 defect detection model is proposed to be applied to the production inspection of display panels. First, in the feature extraction stage, the model constructs a feature fusion module by introducing the FBSSDA attention mechanism to extract foreground and background difference features at the same scale; second, a contrast loss function is introduced to expand the differences between different defect categories; finally, an improved NMS algorithm, U‐NMS, is proposed to increase the recognition of unknown defects.