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Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network

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Abstract

Hot-rolled strip steel is an extremely important industrial foundational material. The rapid and precise identification of surface defects in hot-rolled strip steel is beneficial for enhancing the quality of steel materials and reducing economic losses. Current research primarily focuses on using convolutional neural networks (CNNs) for strip steel surface defect identification. Although the accuracy of identification has remarkably improved in comparison with traditional machine learning methods, it has overlooked issues related to dataset preprocessing and the problem of nonlightweight CNN models with large model parameters and high computational complexity. To address the abovementioned issues, this study proposes a hot-rolled steel strip surface defect identification method based on random data balancing and the lightweight CNN MobileNet-Pro. Random data balancing employs image augmentation to eliminate the differences in the quantity of categories between the hot-rolled strip steel surface defect data, providing diverse images to alleviate overfitting during model training. MobileNet-Pro is used to increase the model’s effective receptive field. Building upon MobileNetV1, it introduces large convolutional kernels and improves depth-wise separable convolution. Experiments show that the new MobileNet-Pro, after random data balancing on the X-SDD dataset, achieves an accuracy of 96.47%, surpassing RepVGG + SA (95.10% accuracy, nonlightweight) and ResNet50 (93.86% accuracy, nonlightweight). Additionally, MobileNet-Pro outperforms mainstream lightweight networks from the MobileNet series, ShuffleNetV2, and GhostnetV2 in terms of performance on the CIFAR-100 and PASCAL VOC 2007 datasets, demonstrating excellent generalization capabilities. All our code and models are available on GitHub: https://github.com/OnlyForWW/MobileNet-Pro.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 32201666), the AIMS Commissioned Project (2022340101001837), and the Open Project of Anhui University Power Quality Engineering Research Center, Ministry of Education (KFKT202304).

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WZ and JW provided the theoretical viewpoints in the manuscript; JW completed the validation experiment in the manuscript; PC Organized and led the project; All authors have reviewed and evaluated the manuscript.

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Correspondence to Peng Chen.

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Zeng, W., Wang, J., Chen, P. et al. Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network. SIViP 18, 5775–5786 (2024). https://doi.org/10.1007/s11760-024-03270-6

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