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EGSANet: edge–guided sparse attention network for improving license plate detection in the wild

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Abstract

The license plate (LP) has regular and prominent edge contour, which we believe is a key factor to improve the precision of LP detection. Therefore, we propose a novel Edge-Guided Sparse Attention (EGSA) mechanism, which is composed of an Edge-Guided Component (EGC) and a Sparse Attention Component (SAC) in tandem. EGC is designed as an end-to-end edge-preserving smoothing filter, which makes full use of convolutional neural networks (CNNs) and edge-guided map to enhance the edge contour of LPs and suppress noise effectively. On this basis, the K most relevant features adaptively searched by SAC are aggregated, thereby obtaining useful long-range dependencies. EGSA mechanism pays attention to the distinguishable regions and the important edge characteristics of LPs at the same time, so that the model can locate more precisely in the complex scenes. By evaluating state-of-the-art attention methods and LP detection models on both CCPD and AOLP datasets, we demonstrate the high precision and strong generalization capability of our proposed method.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangdong, China

    Jing Liang, Guancheng Chen, Yan Wang & Huabiao Qin

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  1. Jing Liang
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  2. Guancheng Chen
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  3. Yan Wang
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  4. Huabiao Qin
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Correspondence to Huabiao Qin.

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Liang, J., Chen, G., Wang, Y. et al. EGSANet: edge–guided sparse attention network for improving license plate detection in the wild. Appl Intell 52, 4458–4472 (2022). https://doi.org/10.1007/s10489-021-02628-4

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