Efficient Fused-Attention Model for Steel Surface Defect Detection

Abstract

Steel surface defect detection is an essential quality control task in manufacturing. As patterns of defects may be viewed as an object, some current defect detection methods, which have achieved promising performance, have been developed based on object-detection models. However, most of these defect detection methods simply incorporate additional heavy modules to improve the accuracy. These methods do not consider the efficiency of the models or the characteristics of the defects. In this paper, we focus on three challenges of steel surface defect detection, which are scale variations, shape variations, and detection efficiency. To address these challenges, we propose a fused-attention network (FANet) for detecting various steel surface defects. Specifically, we propose a fused-attention framework for efficiently detecting defects. This framework applies an attention mechanism to a single balanced feature map, rather than multiple feature maps. This can improve the accuracy and preserve the detection speed of the detection network. To handle defects with multiple scales, we propose an adaptively balanced feature fusion (ABFF) method that can fuse features with suitable weights. It can enhance the discriminative power of the feature maps for detecting defects of different scales. Moreover, we propose a fused-attention module (FAM) to deal with the shape variations of defects. This module can enhance the channel and spatial feature information to perform precise localization and classification of defects with shape variations. Experimental results on two steel surface defect detection datasets, NEU-DET and GC10-DET, demonstrate that our proposed method can achieve state-of-the-art performance.