Harmonizing for defect visibility with Fine-Grained Hierarchical Interaction Learning

Zhongze Wu, Yitian Long, Xiu Su, Yueyi Luo, Shan You, Jun Long

Abstract: Defect detection is a fundamental task in industrial image analysis, crucial for identifying and delineating defect regions. However, existing models, often struggle to learn critical features effectively under conditions of noisy interference. In this study, we introduce the Fine-Grained Hierarchical Interaction Learning (FINet) framework, designed to enhance the learning process by harmonizing feature interactions at multiple scales. Specifically, FINet incorporates the Adaptive Tensor Interaction (ATI) to facilitate high-order feature interactions amidst noise in a high-dimensional frequency space. Additionally, the FlexiFocus network is developed to dynamically balance feature focus across scales, further enhancing defect feature visibility and providing an effective trade-off between computational speed and performance. Extensive experiments on the PVEL-AD dataset show FINet’s superior accuracies (90.50% mAP50, 62.80% mAP50:5:95 ), surpassing DDQ-DETR by 8.3% and 3.6%, respectively. The code is available at https://github.com/zhongzee/FINet-master.