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ORION: Orientation-Sensitive Object Detection

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Object detection is of great importance to intelligent stockbreeding applications, but currently, basic tools like models and datasets are still in shortage in specific livestock breeding circumstances. In this paper, we build a cattle object detection dataset with real oriented bounding box (ROBB) annotation. In particular, this dataset is single-category, multiple-instance per frame, and has body-direction-aligned orientation and non-rigid targets. Benchmark models are investigated with our proposed orientation-sensitive IOU algorithm \(COS\text {-}IOU\) and angle-related loss CosAngleLoss. The combination of these two modules outperforms baseline IOU algorithms and MSE angle loss in a more strict angle-confined criterion. This work is a pioneering exploration in non-rigid oriented object detection with orientation in \([0,2\pi )\), it will shed light on similar issues with single-category, non-rigid, oriented object detection in the stockbreeding and manufacturing industry. Code is available at https://github.com/guowenk/cattle-robb and the dataset will be released to the public later.

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Notes

  1. 1.

    https://github.com/cgvict/roLabelImg.

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Author information

Authors and Affiliations

  1. Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen, 518055, China

    Jingran Xia, Guowen Kuang, Xu Wang & Jinfeng Yang

  2. School of Electronics and Information Engineering, University of Science and Technology Liaoning, Liaoning, 114045, China

    Jingran Xia & Zhibin Chen

Authors
  1. Jingran Xia
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  2. Guowen Kuang
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  3. Xu Wang
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  4. Zhibin Chen
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  5. Jinfeng Yang
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Corresponding author

Correspondence to Guowen Kuang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Xia, J., Kuang, G., Wang, X., Chen, Z., Yang, J. (2022). ORION: Orientation-Sensitive Object Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_47

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_47

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