Improved Siamese Network for High-Resolution Printed Surface Defect Detection Under Few-Shot Conditions

Abstract

Printed product surface defect inspection faces critical challenges in real industrial production, including insufficient detection accuracy, poor robustness, and scarcity of labeled defective samples. This paper proposes an improved Siamese network for high-resolution printed image defect detection to address the above limitations. Original 1300 × 460-pixel printed images are segmented into 64 × 64 patches to construct a training and testing dataset. We optimize the feature measurement strategy of contrastive loss and integrate a position matching module and multi-threshold evaluation strategy to balance detection precision and stability. Quantitative experiments demonstrate that the proposed method achieves an overall accuracy of 94.35%, a precision of 91.99%, a recall of 95.17%, and an F1-score of 93.55% at the fixed threshold of 0.45. Compared with mainstream lightweight detection models including YOLOv8Lite (You Only Look Once version 8 Lite) and MobileViTv2 (Mobile Vision Transformer version 2), our method maintains a stable F1-score advantage of 10.84–24.36% under optimal threshold settings. The proposed approach achieves outstanding performance and robustness in few-shot scenarios and can be deployed for automatic quality inspection of printed parts used in power communication equipment.