High-Accuracy Woven Fabric Recognition with Optimized ResNet50 and ConvNeXt

Traditional woven fabric identification heavily depends on manual experience and subjective judgment, which limits its applicability in modern intelligent textile manufacturing. To address this issue, this paper proposes improved woven fabric recognition approaches based on the ResNet50 and ConvNeXt architectures. For the classical ResNet50, the squeeze-and-excitation network (SENet) and the convolutional block attention module (CBAM) attention mechanisms are integrated separately to boost feature representation, and adopt the Adam optimizer to accelerate convergence. For ConvNeXt, we optimize the network stacking blocks and design a warmup + cosine annealing learning rate scheduling strategy. With the transfer learning strategy, classification experiments are conducted on a self-constructed fabric dataset. The improved ResNet50 model achieves a recognition accuracy of 90.15%, while the optimized ConvNeXt model reaches 90.01%. The models outperform their baseline counterparts, demonstrating the effectiveness of the proposed improvements. This study provides a feasible reference for the research of automatic woven fabric classification and related intelligent textile inspection.