Shuxin Yang
ABSTRACT
In response to the challenges posed by low detection accuracy resulting from a wide range of surface defects, intricate textures, and minute defect targets in steel surfaces, this paper introduces an innovative defect detection model called DCS-YOLOv8, which builds upon the foundation of YOLOv8. Firstly, Real-ESRGAN (Real-Enhanced Super-Resolution GAN) is used to enhance image resolution, effectively addressing the challenge of identifying minuscule defects within the dataset. Furthermore, DCN (Deformable Convolutions) are seamlessly integrated into the backbone network to amplify the network's capability for multi-scale feature extraction, which empowers the network to adeptly navigate intricate background information and concentrate on pinpointing target objects. Lastly, to tackle the issues of elevated false negative rates and diminished detection precision, this paper designs a module based on the CBAM (Concentration-Based Attention Module) and SCSE (Concurrent Spatial and Channel Squeeze and Excitation) attention modules. It facilitates comprehensive information acquisition, enriches the fusion of channel and spatial features, and elevates feature map expression. Regarding experimental outcomes, the enhanced YOLOv8 algorithm shows outstanding detection performance, achieving 78.6% mAP on the NEU-DET dataset, which marks a 4.4% enhancement over the original YOLOv8 network. Notably, the algorithm attains a detection speed of 143 FPS. When juxtaposed with other prominent object detection algorithms, it unequivocally affirms the efficacy and supremacy of this approach, underscoring its potential significance in industrial applications.
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Index Terms
- DCS-YOLOv8: An Improved Steel Surface Defect Detection Algorithm Based on YOLOv8
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