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DetOH: An Anchor-Free Object Detector with Only Heatmaps

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14177))

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Abstract

In object detection, the anchor-based method relies on too much manual design, and the training and prediction process is too inefficient. In recent years, one-stage anchor-free methods such as Fully Convolutional One-stage Object Detector (FCOS) and CenterNet have made a splash in object detection. They not only have a simple structural design, but also demonstrate competitive performance. They have exceeded many two-stage or anchor-based approaches. However, in industrial applications, the design of its multiple output heads hinders the installation of the model. At the same time, different output heads mean a combination of multiple loss functions. This introduces problems in training. Here, we propose an anchor-free object Detector with Only Heatmaps (DetOH) to solve object detection. Bounding box parameters are calculated by post-processing. The design of the single output head allows object detection to use a semantic segmentation network, realizing the unification of the two frameworks. In addition, compared with CenterNet, we have greatly improved the speed of object detection (6 vs. 32 Frames Per Second) with 3.2% Average Precision boost. The proposed DetOH framework can be applied to multi-target tracking, key point detection and other tasks.

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Acknowledgments

This work was supported inpart by the National Natural Science Foundation of China (61972219), the Research and Development Program of Shenzhen (JCYJ20190813174403598), the Overseas Research Cooperation Fund of Tsinghua Shenzhen International Graduate School (HW2021013), the Guangdong Basic and Applied Basic Research Foundation (2022A1515010417), the Key Project of Shenzhen Municipality (JSGG20211029095545002), the Science and Technology Research Project of Henan Province(222102210096).

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Authors and Affiliations

  1. Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

    Ruohao Wu & Xi Xiao

  2. Beijing Research Institute of Telemetry, Beijing, 100076, China

    Hanqing Zhao, Han Zhang & Yongqing Peng

  3. Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Guangwu Hu

Authors
  1. Ruohao Wu
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  2. Xi Xiao
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  3. Guangwu Hu
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  4. Hanqing Zhao
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  5. Han Zhang
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  6. Yongqing Peng
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Corresponding author

Correspondence to Xi Xiao .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Wu, R., Xiao, X., Hu, G., Zhao, H., Zhang, H., Peng, Y. (2023). DetOH: An Anchor-Free Object Detector with Only Heatmaps. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-46664-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46663-2

  • Online ISBN: 978-3-031-46664-9

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