Abstract
Fabric defect detection methods based on deep networks are widely used in the textile industry, but they often suffer from poor model generalization and blurry edge detection. To resolve these challenges, we propose a novel network called “U-SMR Net”, which integrates global contextual features, defect detail features, and high-level semantic features through the combination of ResNet-50 and Swin Transformer modules. Our U-SMR network includes a lightweight multiscale feature extraction module, the dual-branch pyramid Module (DBPM), which is nested to preserve high-resolution, shallow semantic information. We propose a recursive multi-level residual decoding block for multiscale fusion to refine, filter, and enhance input characteristics, generating prediction maps at multiple stages, and by employing an improved binary cross entropy loss function to supervise saliency mapping. The experimental results based on four groups from ZJU-Leaper dataset demonstrate the superior performance of our approach compared to other competitive methods by achieving an average fmeasure score of 75.33%, and finally testing results from both ZJU-Leaper-Total dataset and the HKU-Fabric dataset further support our U-SMR Net’s validity and generalization ability.
Published Version
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