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Feature Enhancement and Reconstruction for Small Object Detection

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MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13833))

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Abstract

Due to the small size and noise interference, small object detection is still a challenging task. The previous work can not effectively reduce noise interference and extract representative features of the small object. Although the upsampling network can alleviate the loss of features by enlarging feature maps, it can not enhance semantics and will introduce more noises. To solve the above problems, we propose CAU (Content-Aware Upsampling) to enhance feature representation and semantics of the small object. Moreover, we propose CSA (Content-Shuffle Attention) to reconstruct robust features and reduce noise interference using feature shuffling and attention. Extensive experiments verify that our proposed method can improve small object detection by 2.2% on the traffic sign dataset TT-100K and 0.8% on the object detection dataset MS COCO compared with the baseline model.

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Notes

  1. 1.

    Please refer to https://github.com/ultralytics/yolov5. For TT-100K, we use the large YOLOv5l6 as the baseline model. For MS COCO, we use the small YOLOv5s as the baseline model. Except for CSA and CAU, our model is the same as the official model. The parameters of YOLOv5l6 are about 10\(\times \) of YOLOv5s.

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Acknowledgment

The research is supported by National Key Research and Development Program of China (2020AAA0109701), National Natural Science Foundation of China (62076024, 62006018).

Author information

Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, 100083, China

    Chong-Jian Zhang, Song-Lu Chen, Qi Liu, Zhi-Yong Huang & Xu-Cheng Yin

  2. USTB-EEasyTech Joint Lab of Artificial Intelligence, Beijing, 100083, China

    Chong-Jian Zhang, Song-Lu Chen, Qi Liu, Zhi-Yong Huang, Feng Chen & Xu-Cheng Yin

  3. EEasy Technology Company Ltd., Zhuhai, 519000, China

    Feng Chen

Authors
  1. Chong-Jian Zhang
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  2. Song-Lu Chen
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  3. Qi Liu
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  4. Zhi-Yong Huang
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  5. Feng Chen
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  6. Xu-Cheng Yin
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Corresponding author

Correspondence to Xu-Cheng Yin .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

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Zhang, CJ., Chen, SL., Liu, Q., Huang, ZY., Chen, F., Yin, XC. (2023). Feature Enhancement and Reconstruction for Small Object Detection. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-27077-2_2

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