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YOLO-SS: optimizing YOLO for enhanced small object detection in remote sensing imagery

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Abstract

The identification of minuscule objects in remote sensing data presents a formidable challenge in computer vision, where objects may occupy a mere handful of pixels. The lack of unique shape features in such small objects hinders the effectiveness of established object detection algorithms. Remote sensing of small object detection plays an important role in areas such as environmental monitoring and estimating agricultural production. To address this challenge, in this study, we introduce YOLO-SS, an enhanced version of the YOLO algorithm tailored specifically for small object detection in remote sensing imagery. YOLO-SS incorporates an optimized backbone network, a restructured loss function and an asymmetric training sample weighting strategy. These improvements prioritize the model’s attention toward high-quality positive samples of small objects while reducing sensitivity to complex backgrounds. Evaluation on the AI-TOD dataset demonstrates YOLO-SS’s exceptional performance, achieving an AP50 score of 0.535, surpassing YOLOv6L by 13.4% and other popular object detection algorithms. Our findings offer a novel pathway for advancing small object detection capabilities in diverse remote sensing applications.

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Data availability

The AI-TOD dataset is publicly available. The AI-TOD dataset is ethical, and the paper is not conflict of interest. The link to the dataset is https://github.com/jwwangchn/AI-TOD. The datasets used and analyzed during the current study are available from the corresponding author or first author on reasonable request.

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Author information

Authors and Affiliations

  1. Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an, 710000, Shaanxi, China

    Qiang Tang, Chang Su, Yuan Tian, Shibin Zhao, Kai Yang, Wei Hao, Xubin Feng & Meilin Xie

  2. University of Chinese Academy of Sciences, Beijng, 100049, China

    Qiang Tang, Chang Su, Yuan Tian, Shibin Zhao, Kai Yang, Wei Hao, Xubin Feng & Meilin Xie

Authors
  1. Qiang Tang
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  2. Chang Su
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  3. Yuan Tian
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Contributions

Qiang Tang assisted with methodology, algorithm, writing and editing, Chang Shang helped with algorithm and writing, Kai Yang, Yuan Tian and Shibin Zhao carried out ablation study, and Wei Hao, Xubin Feng and Meilin Xie were responsible for financial support, laboratory equipment and experiment guidance.

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Correspondence to Meilin Xie.

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Tang, Q., Su, C., Tian, Y. et al. YOLO-SS: optimizing YOLO for enhanced small object detection in remote sensing imagery. J Supercomput 81, 303 (2025). https://doi.org/10.1007/s11227-024-06765-8

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