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ORIGINAL RESEARCH article

Front. Phys.

Sec. Social Physics

Volume 13 - 2025 | doi: 10.3389/fphy.2025.1553224

An Improved Lightweight Tiny-Person Detection Network Based on YOLOv8: IYFVMNet

Provisionally accepted
Fan Yang Fan Yang 1*Lihu Pan Lihu Pan 1Hongyan Cui Hongyan Cui 1Linliang Zhang Linliang Zhang 2
  • 1 Taiyuan University of Science and Technology, Taiyuan, China
  • 2 Shanxi Intelligent Transportation Institute Co., Ltd., Taiyuan, China

The final, formatted version of the article will be published soon.

    In the aviation field, drone search and rescue is a highly urgent task involving small target detection. In such a resource-constrained scenario, there are challenges of low accuracy and high computational requirements. This paper proposes IYFVMNet, an improved lightweight detection network based on YOLOv8. The key challenges include feature extraction for small objects and the trade-off between detection accuracy and speed. To address these, four major innovations are introduced: (1) Fasternet is used to improve the bottleneck structure in the cross-stage feature fusion backbone network. This approach fully utilizes all feature map information while minimizing the computational and memory requirements. (2) the neck network structure is optimized using the Vovnet Gsconv Cross Stage Partial module. This operation also reduces the computational cost by decreasing the amount of required feature map channels, while maintaining the effectiveness of the feature representation. (3) he Minimum Point Distance Intersection over Union loss function is employed to optimize bounding box detection during model training. (4) to construct the overall network structure, the Layer-wise Adaptive Momentum Pruning algorithm is used for thinning. Experiments on the TinyPerson dataset demonstrate that IYFVMNet achieves a 46.3% precision, 30% recall, 29.3% mAP50, and 11.8% mAP50-95. The model exhibits higher performance in terms of accuracy and efficiency when compared to other benchmark models, which demonstrates the effectiveness of the improved algorithm (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) in small-object detection and provides a reference for future research.

    Keywords: Small object detection, YOLOv8 algorithm, FasterNet, vovnet gsconv, LAMP

    Received: 30 Dec 2024; Accepted: 27 Mar 2025.

    Copyright: © 2025 Yang, Pan, Cui and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fan Yang, Taiyuan University of Science and Technology, Taiyuan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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