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Visible and Clear: Finding Tiny Objects in Difference Map

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15075))

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Abstract

Tiny object detection is one of the key challenges for most generic detectors. The main difficulty lies in extracting effective features of tiny objects. Existing methods usually perform generation-based feature enhancement, which is seriously affected by spurious textures and artifacts, making it difficult to make the tiny-object-specific features visible and clear for detection. To address this issue, we propose a self-reconstructed tiny object detection (SR-TOD) framework. We for the first time introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. This inspires us to enhance the weak representations of tiny objects under the guidance of the difference maps. Thus, improving the visibility of tiny objects for the detectors. Building on this, we further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear. In addition, we further propose a new multi-instance anti-UAV dataset. Extensive experiments demonstrate our effectiveness. The code is available: https://github.com/Hiyuur/SR-TOD.

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Acknowledgements

This work was sponsored in part by the National Key R&D Program of China 2022ZD0116500, in part by the National Natural Science Foundation of China (62106171, 62222608, U23B2049, 61925602), in part by the Haihe Lab of ITAI under Grant 22HHXCJC00002, in part by Tianjin Natural Science Funds for Distinguished Young Scholar under Grant 23JCJQJC00270, and in part by the Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications under Grant BDIC-2023-A-008. This work was also sponsored by CAAI-CANN Open Fund, developed on OpenI Community.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Bing Cao, Haiyu Yao, Pengfei Zhu & Qinghua Hu

  2. Tianjin Key Lab of Machine Learning, Tianjin, China

    Bing Cao, Haiyu Yao, Pengfei Zhu & Qinghua Hu

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  1. Bing Cao
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  2. Haiyu Yao
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  3. Pengfei Zhu
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  4. Qinghua Hu
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Correspondence to Pengfei Zhu .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Cao, B., Yao, H., Zhu, P., Hu, Q. (2025). Visible and Clear: Finding Tiny Objects in Difference Map. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15075. Springer, Cham. https://doi.org/10.1007/978-3-031-72643-9_1

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