YOLOvT: CSPNet-based attention for a lightweight textile defect detection model
Xiaohan Hu,
Ning Dai,
Xudong Hu
et al.
Abstract:Fabric inspection is a crucial process in the textile industry's quality control. Due to the varying structures, textures, geometric features, and spatial distributions of fabric defects, manual fabric inspection is costly and inefficient. Existing fabric defect detection algorithms struggle to strike a balance among efficiency, accuracy, applicability, and deployment costs. In this model, an efficient lightweight fabric defect detection and classification algorithm based on deep convolutional neural networks … Show more
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