TOF-UNet: High-precision method for terry towel defect detection

Abstract

Towel defect detection mostly relies on manual labor, but there are problems such as a low efficiency and high missed detection rate. Therefore, automatic detection of towel defects is becoming increasingly popular. Although the UNet-based method has been successful, there are problems that must be solved for practical applications. To address the problems of the complex background caused by loops on the towel surface, relatively small defect size, and imbalanced defect–background ratio, a high-precision convolutional neural network is proposed, which is called tiny object-focused UNet. A coordinate attention mechanism is introduced in tiny object-focused UNet to enhance the feature-extraction capabilities, and spatial pyramid pooling is employed to fuse local and global information for more accurately extraction of towel defect features. Finally, the composite loss function obtained via the addition of the cross-entropy loss and the Dice loss function is used to reduce the impact of the imbalance in the proportion of defects on the detection accuracy. The proposed model is evaluated using a self-made dataset. The experimental results indicate that the segmentation performance of the network is better than that of other networks. Thus, the proposed method is useful for segmenting towel defects.