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An improved residual learning model and its application to hardware image classification

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Abstract

Some hardware is similar in color and shape between different classes, and some hardware varies within a class, thereby decreasing the accuracy of classification models. The baseline of image classification depends on network architectures. To solve this problem, this paper proposes an improved residual network architecture that achieves high accuracy in hardware classification. This paper proposes a hardware dataset that fills the blank of hardware images. The improved residual network architectures are based on depthwise over-parameterized convolution and pyramidal convolution. This model could obtain information from different levels and channels. Moreover, compared with the novel architectures, our model does not require increasing the computation. Experiments on the hardware dataset show that our neural network architectures achieved better accuracy compared with notable architectures such as ResNet50 and DenseNet121. The classification accuracy of the proposed network is 94.6% and the f1 score is 90.26%. To verify the proposed network architecture, we applied our model in fine-grained datasets; our model achieved the best performance in the same training setting.

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Due to the nature of this research, participants of this paper did not agree for their data to be shared publicly, so supporting data is not available.

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Acknowledgements

This work was supported by the Key R&D Program of Jiang** Province of China (Grant Nos.20181BBG70031) and the National Natural Science Foundation of China(62066027).

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Authors and Affiliations

  1. Department of Computer, School of Mathematics and Computer Sciences, Nanchang University, Xuefu Road, Nanchang, 330031, China

    Zhentao Zhang, Wenhao Li, Yuxi Cheng, Qingnan Huang & Taorong Qiu

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  1. Zhentao Zhang
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  2. Wenhao Li
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  3. Yuxi Cheng
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  4. Qingnan Huang
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Contributions

Zhentao Zhang, Wenhao Li and Yuxi Cheng contributed equally and consistently to this manuscript. Qingnan Huang and Taorong Qiu provided useful help to this manuscript. Correspondence should be addressed to Taorong Qiu: qiutaorong@ncu.edu.cn.

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Correspondence to Taorong Qiu.

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Zhang, Z., Li, W., Cheng, Y. et al. An improved residual learning model and its application to hardware image classification. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03340-3

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