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Low-res MobileNet: An efficient lightweight network for low-resolution image classification in resource-constrained scenarios

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Abstract

The convolutional neural networks (CNNs) deployed on devices for visual image processing faces the thorny problems on high system real-time requirements and resource consumption. A high-performance Low-res MobileNet model is constructed to effectively alleviate the high computing resources and storage costs in the real-time image processing. The main works are summarized as: (1) To actively match the input of low-resolution feature map, the MobileNetV2 is further optimized by clipping to simplify the network structure and improve the efficiency of image recognition. (2) To improve the classification accuracy, the Inception structure is used to fill the Dwise layer in depthwise separable convolution to extract more abundant low-resolution features; the activation function during the process of increasing the dimension is replaced to avoid the loss of useful information; Inter-layer connection structure is adopted to strengthen the fusion of feature information between layers. (3) To reduce the network scale, the gradually decreasing expansion factors are used to remove the redundant structure of the model. Subsequently, the Low-res MobileNet is validated and evaluated through data sets of different scales. The experimental results show that this model has smaller scale, less computation and higher classification accuracy compared with other CNN models. The model has 0.36 M parameters and 25.46 M floating point of operations (FLOPs), which is easy to deploy to resource-constrained mobile and embedded devices. The model runs at 35 batches per second, and it achieves an accuracy rate of 89.38%, 71.60%, and 87.08% on CIFAR-10, CIFAR-100, and CINIC-10 datasets, respectively, which is basically suitable for real-time image classification task applied in low-resolution application scenarios.

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Acknowledgments

This research work was supported by National Natural Science Foundation of China (61001049) and Beijing Natural Science Foundation (4172010).

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  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Haiying Yuan, Junpeng Cheng, Yanrui Wu & Zhiyong Zeng

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  1. Haiying Yuan
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  2. Junpeng Cheng
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  4. Zhiyong Zeng
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Correspondence to Haiying Yuan.

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Yuan, H., Cheng, J., Wu, Y. et al. Low-res MobileNet: An efficient lightweight network for low-resolution image classification in resource-constrained scenarios. Multimed Tools Appl 81, 38513–38530 (2022). https://doi.org/10.1007/s11042-022-13157-8

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  • DOI: https://doi.org/10.1007/s11042-022-13157-8

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