Abstract
Efficient models for text detection have a wide range of applications. However, existing real-time text detection methods mainly focus on the inference speed without considering the number of parameters. In this paper, we propose an efficient, lightweight network called ShallowNet with fewer layers, which significantly reduces the number of parameters and improves the inference speed. Fewer layers usually incur small receptive fields that degrade the performance of large-size text detection. To address such an issue, we utilize dilated convolution with various receptive fields that satisfies the need for different sizes of text detection. Nevertheless, the design of dilated convolution ignores the continuity property of text and results in stickiness and fragment in the text detection tasks. To tackle this challenge, we introduce instance count-aware supervision information that guides the network focus on text instances and preserves the boundaries. The instance count-aware supervision is used as the weight of the loss function. Extensive experiments demonstrate that the proposed method achieves a desirable trade-off between the accuracy, size of the model, and inference speed.
This paper is supported by NSFC (No. 62176155, 61772330, 61876109), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), the Shanghai Key Laboratory of Crime Scene Evidence (no. 2017XCWZK01), and the Interdisciplinary Program of Shanghai Jiao Tong University (no. YG2019QNA09).
H. Lu—Also with the MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University.
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Hu, X., Wu, D., Li, H., Jiang, F., Lu, H. (2021). ShallowNet: An Efficient Lightweight Text Detection Network Based on Instance Count-Aware Supervision Information. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_52
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