A Strip Steel Surface Defect Salient Object Detection Based on Channel, Spatial and Self-Attention Mechanisms

• Published in: Electronics (Basel) 2024-11, Vol.13 (21), p.4277
• Main Authors: Sun, Yange, Geng, Siyu, Guo, Huaping, Zheng, Chengyi, Zhang, Li
• Format: Article
• Language: English
• Subjects: Algorithms; Coordinate transformations; Datasets; Decoding; Deep learning; Defects; Detectors; Feature extraction; Feature maps; Image enhancement; Inclusions; Machinability; Manufacturing; Metal strips; Neural networks; Object recognition; Salience; Semantics; Sensors; Service life; Steel products; Strip steel; Surface defects; Surface layers; Transportation equipment industry; Watersheds
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13214277

Strip steel is extensively utilized in industries such as automotive manufacturing and aerospace due to its superior machinability, economic benefits, and adaptability. However, defects on the surface of steel strips, such as inclusions, patches, and scratches, significantly affect the performance and service life of the product. Therefore, the salient object detection of surface defects on strip steel is crucial to ensure the quality of the final product. Many factors, such as the low contrast of surface defects on strip steel, the diversity of defect types, complex texture structures, and irregular defect distribution, hinder existing detection technologies from accurately identifying and segmenting defect areas against complex backgrounds. To address the above problems, we propose a novel detector called S3D-SOD for the salient object detection of strip steel surface defects. For the encoding stage, a residual self-attention block is proposed to explore semantic information cues of high-level features to locate and guide low-level feature information. In addition, we apply a general residual channel and spatial attention to low-level features, enabling the model to adaptively focus on the key channels and spatial areas of feature maps with high resolutions, thereby enhancing the encoder features and accelerating the convergence of the model. For the decoding stage, a simple residual decoder block with an upsampling operation is proposed to realize the integration and interaction of feature information between different layers. Here, the simple residual decoder block is used for feature integration due to the following observation: backbone networks like ResNet and the Swin Transformer, after being pretrained on the large dataset ImageNet and then fine-tuned on a smaller dataset for strip steel surface defects, are capable of extracting feature maps that contain both general image features and the specific characteristics required for the salient object detection of strip steel surface defects. The experimental results on the SD-saliency-900 dataset show that S3D-SOD is better than advanced methods, and it has strong generalization ability and robustness.