An Optical Lens Defect Detection Method for Micro Vision Based on WGSO-YOLO

This paper proposes an improved defect detect algorithm named WGSO-YOLO for real-time detection of optical lens surface defects to solve the slow detection speed, low detection accuracy and unbalanced datasets commonly observed in optical lens surface defect detection. The proposed algorithm replaces the standard convolution with the GSConv module in the network feature fusion part, reducing the number of parameters and computational complexity without significant loss of contribution. It incorporates second-order channel attention mechanism to enhance the model's feature extraction and fusion capabilities. In the prediction phase, the CIoU loss function is replaced with the WIoU loss function to improve the model's generalization ability. The experiments demonstrate that our model performs exceptionally well on the optical lens surface defect dataset, achieving an FPS of 96 and an mAP@.5 of 0.927.