An online automatic carbide insert high-resolution surface defect detection system based on template-guided model

Carbide insert is a fundamental tool in manufacturing, and it has been widely applied to cut raw materials or machine workpieces. In the production process of carbide insert, surface defect detection system plays a crucial role. Current general-purpose defect detection methods remain challenging due to the low efficiency of high resolution images and high diversity of carbide inserts, which affect the practical application in manufacturing. In this paper, we propose a specifically designed carbide insert defect detection algorithm based on template guided framework called TG-Net to address these issues. In contrast to previous general-purpose approaches that merely encode the defect image, we innovatively utilize a template image to guide the entire prediction. First, a siamese lightweight network is employed to extract multi-level features of the reference and defect image-pair. Then, the context and template guided attention module is adopted to fuse adjacent feature maps guided by difference maps at all levels, which promotes effective information to propagate from high-level feature maps to low-level ones. Benefiting from learning the difference information between image-pair, our algorithm can rapidly generalize to new types of carbide inserts without training again. On our carbide insert dataset, the proposed method yields the best prediction accuracy of 38.80% with the least parameters and reaches a real-time inference speed of 5.03 frames per second (FPS) on an image of 5120 x 5120, indicating that our approach achieves a trade-off between accuracy and efficiency when handling high-resolution images. Furthermore, a hardware carbide insert detection system is proposed, integrating the TGNet algorithm and deployed in the practice of production, demonstrating the effectiveness of our system.