LSA-Net: Location and shape attention network for automatic surface defect segmentation

Neural network algorithms for segmenting defects are widely used in industrial production. However, how to fuse the location information of defects in a single model and avoid the features extracted by different submodules gradually tend to be similar during the training is still a problem. To solve these problems, an end-to-end network is proposed that focuses on defect location and shape features, which can guarantee the difference between features extracted by different submodules. In the encoding stage, the location attention module enhances the perception of defect locations. The shape detection module with feature difference loss is designed to strengthen the detection of defect shapes. In the decoding stage, the features of different scales are fused to obtain the final defect region. The experimental results confirm the effectiveness of the proposed location and shape detection modules in the intersection over union on four datasets.

Automated summary

This article proposes an end-to-end network to solve the problem of defect location and shape feature fusion. The location attention module enhances the perception of defect locations, while the shape detection module with feature difference loss strengthens the detection of defect shapes. In the decoding stage, the features of different scales are fused to obtain the final defect region. The experimental results confirm the effectiveness of the proposed location and shape detection modules in the intersection over union on four datasets.