关键词: 小样本学习, 缺陷分类, 双分支结构, 通道注意力, 特征融合

Abstract: To address the challenges of complex backgrounds in small-sample defect images, making it difficult to distinguish fine-grained features, and the inability to directly apply pre-trained features for defect classification, an adaptive feature filtering and reorganization method for few-shot surface defect classification was proposed. A dual-branch parallel adapter is constructed between the pre-trained feature extraction module and the classifier to achieve precise feature enhancement: First, channel attention was used to capture cross-channel interaction information, suppressing redundant background noise while maintaining dimensionality; second, multi-path parallel convolution was employed to structurally reorganize the feature sequence, strengthening the discriminative power of local and global semantics; finally, an adaptive fusion mechanism was introduced to dynamically adjust the contribution weights of the two branches. In the 5-shot tasks on the NEU-DET, PCB, and MTD datasets, the proposed method achieves accuracies of 91.3%, 64.3%, and 80.8%, respectively, outperforming current mainstream small-sample classification models such as CLIP-ProtoNet and CLIP-Adapter. In the 1-shot and 5-shot tasks on the MVTec-FS dataset, the average accuracy of the proposed method is improved by 2.1 and 2.3 percentage points compared to the benchmark method MVREC (Multi-View Region Context), respectively. Experimental results show that the proposed method effectively improves the classification accuracy of surface defects in small samples and exhibits good generalization ability.

Key words: few-shot learning, defect classification, dual-branch structure, channel attention, feature fusion