Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects.

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method.