A New Multiscale Multiattention Convolutional Neural Network for Fine-Grained Surface Defect Detection

Surface defect detection (SDD) is essential in industry to ensure the quality of products. Fine-grained SDD has been developed to cope with the high intraclass and low interclass differences in defect samples. However, the sizes and appearances of the defects are various, making it still challenging to identify the defects accurately. To overcome this drawback, this article proposes a new multiscale multiattention convolutional neural network (MSMA-SDD) for fine-grained SDD. First, a new multiscale CNN network is developed, which uses features from different layers to match the defects with different sizes. Second, a new multiattention module is proposed, which generates compact attention map to assist MSMA-SDD focusing on the tiny defects. Third, the multiscale and multiattention-based MSMA-SDD is adopted in the fine-grained SDD task. The proposed MSMA-SDD is conducted on three datasets. The experimental results show that MSMA-SDD is superior to the current most advanced method, and the accuracy are 100%, 99.59%, and 99.57%, respectively, which has verified its potential in the SDD field.