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Defect detection as a part of industrial production is essential to monitor product quality. In the last decade, convolutional neural networks have been widely used in industrial defect detection, but existing CNNS-based fabric defect detection models suffer from the problems of not fully utilizing contextual information and inadequate characterization of the underlying features. To address the issues above, we propose a novel hierarchical feature fusion network with receptive field block is proposed for fabric defect detection. The low-level features are efficiently characterized by designing the context-aware feature extraction module (CAFEM) with a short connection. Then the novel receptive field block with five branches (RFB-5) can integrate different scale high-level feature maps, and the holistic attention module (HAM) is adopted to focus on the significant information. Moreover, the low-level features are fused with the high-level features to better represent the fabric texture information. Finally, a joint loss with a boundary IoU loss and a cross entropy loss is adopted to guide the network to learn more detailed information. Experimental results conducted on our built fabric image dataset demonstrate that the proposed method makes it possible to locate the defective areas in the fabric, which is superior to the seven existing methods.
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