Surface defect recognition of hot-rolled strip steel based on self-knowledge distillation
Surface defect detection is critical aspect in the production process of hot-rolled strip steel,serving as a key factor in improving the quality of these steel products.Firstly,to address the challenges posed by the diverse morphologies of surface defect images,various interferences,and the need for improved detection accuracy,a self-knowledge distillation framework(SD)was designed to enhance the defect detection accuracy of the model.The rep-resentational information of sample data was increased by employing data augmentation methods such as geometric transformations,color enhancement,and linear interpolation.Additionally,a novel loss function(LCK Loss)was proposed,considering both the label's probability distribution and closeness,enabling the model to better learn the knowledge provided by the samples and facilitating the transfer of information between different representations of the same data,thereby improving the model's generalization ability.Secondly,the Poolformer network was applied to the surface defect detection of hot-rolled strip steel.To address the issue of a large number of parameters in the Poolformer12 network,a lightweight network called LRAM-Poolformer8 was designed,achieving model accelera-tion by reducing network depth and computational complexity.Finally,experiments were conducted on 8-class sur-face defect dataset from WISCO's CSP unit.The results demonstrate that the proposed SD-LRAM-Poolformer8 model achieves average recognition accuracy of 98.20％.Compared to Poolformer12,it shows an improvement of 1.62 percent points in detection accuracy while reducing the computational complexity to only 56.4％of the original model.These findings highlight the feasibility and effectiveness of the new model in surface defect detection of hot-rolled strip steel.
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