This method fully leverages the extracted information and improves image information retention by combining the strengths of each module. Second, we design a parallel multi-scale residual module to adaptively extract diverse discriminative features from the input image, thereby enhancing the model’s feature extraction capability.

Finally, in the downsampling stage, we incorporate a PReLU activation function to mitigate the problem of neuron death during downsampling. We conducted extensive experiments using the NEU-CLS-64 steel surface defect dataset, and the results demonstrate that our model outperforms other methods in terms of detection performance, achieving an average recognition accuracy of 97.5%.

Through ablation experiments, we validated the effectiveness of each module; through visualization experiments, our model exhibited strong classification capability. Additionally, experiments on the X-SDD dataset confirm that the ESG-ConvNeXt network achieves solid classification results. Therefore, the proposed ESG-ConvNeXt network shows great potential in steel surface defect classification.

Read the full article at Nature.com.