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Textureness-Aware Neural Network for Edge Detection

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15034))

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Abstract

Convolutional neural networks have made significant progress in edge detection by progressively exploring the context and semantic features. However, most methods struggle to produce pixel-accurate edge maps and suffer from false positives in the high-frequency texture regions of non-edge part. In this paper, We propose a new edge detection method that aims to adjust the model’s focus on different pixels in the non-edge area based on textureness and enhance the accuracy of edge detection. Specifically, we first propose a weighting strategy based on textureness and obtain a textureness-aware loss RWCE, which can guide the model to pay more attention to the learning of high-frequency texture regions during the training process, thus improving the prediction accuracy of these regions. Moreover, we design an end-to-end network which adopts the bottom-up/top-down architecture, effectively utilizing hierarchical features, progressively increasing the resolution of feature maps, and ultimately generating pixel-accurate edge maps. Our method achieves promising performance on BSDS500, BIPED, NYUD, and outperforming most previous methods. The source code of this work is available at: https://github.com/yx-yyds/TANet.

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Acknowledgement

Supported by the National Natural Science Foundation of China (Grant No.62061049, Grant No.12263008), the Yunnan Provincial Department of Science and Technology-Yunnan University Joint Special Project for Double-Class Construction (Grant No.202201BF070001-005), Yunnan Revitalization Talent Support Program (Grant No.C6213001221).

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Authors and Affiliations

  1. Yunnan University, Kunming, 650000, China

    Xi Yang, Liangfei Cheng, Guowu Yuan & Hao Wu

Authors
  1. Xi Yang
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  2. Liangfei Cheng
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  3. Guowu Yuan
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  4. Hao Wu
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Corresponding author

Correspondence to Hao Wu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Yang, X., Cheng, L., Yuan, G., Wu, H. (2025). Textureness-Aware Neural Network for Edge Detection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15034. Springer, Singapore. https://doi.org/10.1007/978-981-97-8505-6_18

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  • DOI: https://doi.org/10.1007/978-981-97-8505-6_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-8504-9

  • Online ISBN: 978-981-97-8505-6

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