ClothNet: sensitive semantic segmentation network for fabric defect detection

When the conventional semantic segmentation method is applied to fabric defect detection, the omission factor of small size defects is relatively high, and the network model with larger depth is easy to lose the features of small size defects and has poor real-time performance. To address these problems, we propose two sensitive semantic segmentations, ClothNet based on deep feature fusion and ClothNet-tiny based on atrous spatial pyramid pooling. First, in ClothNet, deep and shallow features are fused to compensate for the information loss caused by pooling. Second, ClothNet-tiny is designed to improve the detection speed. Finally, an adaptive loss function for defect size, namely weighted dice loss is proposed. The results on the validation set show that ClothNet achieves 78.8% Mean Intersection over Union mean. Compared to fully convolutional networks, ClothNet reduces memory consumption by 28% and ClothNet-tiny by 77%.