A steel surface defect detection model based on graph neural networks

Abstract Steel is an indispensable raw material in the construction industry. To avert catastrophic events such as building collapse, it is essential to detect minute defects on steel surfaces during production. However, this has been a persistent challenge due to the minuscule and dense nature of these defects. To this end, we propose an efficient defect detector called Vision Grapher with Hadamard, which employs a novel attention mechanism (HDmA) to establish local-to-local relationships within an image and integrates global relationships by graph convolution. With the HDmA module, we can not only fuse information under the same field of view, but also under different fields of view, which significantly enhances the richness of the acquired features. In addition, compared to convolutional neural networks, graph neural networks can utilize the contextual information in the image more effectively and resulting in better performance. We evaluate our model on the NEU-DET and GC-10 benchmark datasets, which encompass six and ten types of defects on the surfaces of hot-rolled and cold-rolled steel, and our model achieves a mean average precision of 79.04% and 66.93% on the two datasets, respectively. The results demonstrate that our model significantly improves the accuracy of defect detection compared to existing methods.