Low-Resolution Magnetic Tile Defect Image Recognition Based on Task-Aware AMCANet

Low-resolution magnetic tile defect recognition suffers from the loss of texture, edge and fine-grained defect information, which weakens discriminative features and reduces recognition accuracy in industrial inspection. To improve defect recognition under low-resolution imaging conditions, this paper proposes a task-aware low-resolution magnetic tile defect image recognition method based on AMCANet. AMCANet is used as the basic super-resolution reconstruction network, and a fixed high-resolution classifier is introduced during training to construct feature consistency loss and classification task loss. Thus, the reconstructed images are constrained to approach high-resolution images at the pixel level while preserving defect-discriminative features. Experiments on the Magnetic Tile Surface Defect Dataset show that the proposed TA-AMCANet achieves an Accuracy of 0.9065 and a Macro-F1 of 0.8204 under the fixed-classifier evaluation setting, improving on the original AMCANet by 0.3238 and 0.3726, respectively. Further ablation, loss-weight sensitivity, AMCA Block configuration, cross-classifier, degradation robustness, external-dataset generalization, Grad-CAM visualization, and computational complexity experiments verify the effectiveness of the proposed task-aware strategy. The results further indicate that higher pixel-level reconstruction metrics do not necessarily correspond to better downstream defect recognition performance, while TA-AMCANet improves recognition adaptability without introducing additional parameters or inference overhead. The method provides an effective solution for low-resolution industrial defect recognition.