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PiDiNeXt: An Efficient Edge Detector Based on Parallel Pixel Difference Networks

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14434))

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Abstract

The Pixel Difference Network (PiDiNet) is well-known for its success in edge detection. Combining traditional operators with deep learning, PiDiNet achieves competitive results with fewer parameters. However, the complex and inefficient choice of traditional edge detection operators hinders PiDiNet’s further development. Therefore, we propose a novel lightweight edge detector called PiDiNeXt, which combines traditional edge detection operators with deep learning-based model in parallel to solve the operators choice problem and further enrich features. The results of experiments on BSDS500 and BIPED datasets demonstrate that PiDiNeXt outperforms PiDiNet in terms of accuracy. Moreover, we employ the reparameterization technique to prevent the extra computational cost caused by the multi-branch construction. This enables PiDiNeXt to achieve an inference speed of 80 FPS, comparable to that of PiDiNet. Furthermore, the lightweight version of PiDiNeXt can achieve an inference speed of over 200 FPS, meeting the needs of most real-time applications. The source code is available at https://github.com/Li-yachuan/PiDiNeXt.

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Acknowledgements

This work is partly supported by National key r &d program (Grant no. 2019YFF0301800), National Natural Science Foundation of China (Grant no. 61379106), the Shandong Provincial Natural Science Foundation (Grant nos. ZR2013FM036, ZR2015FM011).

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Authors and Affiliations

  1. China University of Petroleum (East China), Qingdao, Shandong, 266500, China

    Yachuan Li, Guanlin Li, Chaozhi Yang, Qian Xiao, Yun Bai & Zongmin Li

  2. ESPOCH Polytechnic University, Data Science Research Group, Riobamba, Ecuador

    Xavier Soria Poma

  3. National University of Chimborazo, Riobamba, 060110, Ecuador

    Xavier Soria Poma

Authors
  1. Yachuan Li
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  2. Xavier Soria Poma
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  3. Guanlin Li
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  4. Chaozhi Yang
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  5. Qian Xiao
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  6. Yun Bai
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  7. Zongmin Li
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Corresponding author

Correspondence to Zongmin Li .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, Y. et al. (2024). PiDiNeXt: An Efficient Edge Detector Based on Parallel Pixel Difference Networks. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_22

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  • DOI: https://doi.org/10.1007/978-981-99-8549-4_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8548-7

  • Online ISBN: 978-981-99-8549-4

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