Region-based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Abstract

Next-generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks-based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region-based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region-based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU-DET and GC10-DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted-surface, rolled-in scale and scratches on NEU-DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.