Defect detection of small targets on steel surface based on self-attention feature fusion
In addressing the issue of ineffective extraction of rich defect features for small-scale surface defects on steel due to their low contrast and small proportion,this paper proposes a solution for small-target defect detection.Leveraging the relationship between contextual information integration and enhanced feature fusion,we introduce the following approaches:incorporating the sliding window mechanism Swin Transformer,which integrates feature information from different blocks hierarchically and through local windows to enhance the contrast of defect features while reducing convolutional operation density;the model employs Coordinate Attention to obtain more positional information,enhancing the diversity of features related to small-target defects.Additionally,we propose the steel surface small-target defect detection model SFNet based on self-attention feature fusion,integrating features with richer semantic information across different scales using the CSP-FCN feature fusion module.Experimental results demonstrate that SFNet achieves superior detection performance on the NEU-DET and GC10-DET public datasets compared to current classic object detection models.Furthermore,the proposed model achieves an average precision improvement of 3%and 3.7%,respectively,while reducing the parameter count to half of its original size.
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