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<p>In recent years, with the rapid development and deep integration of UAV technology and deep learning, UAV aerial photography small object detection has been widely used in urban traffic monitoring, disaster relief, military, and other fields. However, the currently popular deep learning methods encounter several challenges when directly applied to small object detection in UAV aerial images. These include difficulties in detecting dense objects, loss of fine-grained information, and inadequate feature representation of small objects. Additionally, there are issues such as mutual dilution and semantic conflicts between adjacent features during multi-level feature fusion. To address these issues, this paper proposes the fine-grained feature preservation and multi-scale feature pyramid balancing network (FMFN-YOLO), which is based on the YOLO architecture. Firstly, we investigated the impact of various scale detection heads on model performance. Based on our findings, we readjusted the network structure, thereby enhancing the model’s ability to detect dense objects. Secondly, we proposed three innovative plug-and-play modules: the fine-grained preservation feature aggregation module (FPFA), the multi-scale feature enhancement module (MFEM), and the feature pyramid balancing module (FPBM). These three modules are respectively used to enhance the network’s capabilities in fine-grained information retention, multi-scale feature extraction, and multi-level feature fusion, while effectively suppressing interference from complex backgrounds. We demonstrate the effectiveness of our method on the VisDrone2019 and AI-TOD datasets, where FMFN-YOLO achieves a mAP50 of 44.5 and 43.6%, respectively. This represents an improvement of 7.6 and 6.4% over the baseline, respectively. Similarly, it offers significant advantages over state-of-the-art methods.</p>

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A UAV aerial image small object detection algorithm based on fine-grained feature preservation and multi-scale feature pyramid balancing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月11日

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  • Jian Luo,
  • Kaixuan Chang,
  • Jianhua Huang,
  • Xiyan Sun,
  • Yuanfa Ji

摘要

In recent years, with the rapid development and deep integration of UAV technology and deep learning, UAV aerial photography small object detection has been widely used in urban traffic monitoring, disaster relief, military, and other fields. However, the currently popular deep learning methods encounter several challenges when directly applied to small object detection in UAV aerial images. These include difficulties in detecting dense objects, loss of fine-grained information, and inadequate feature representation of small objects. Additionally, there are issues such as mutual dilution and semantic conflicts between adjacent features during multi-level feature fusion. To address these issues, this paper proposes the fine-grained feature preservation and multi-scale feature pyramid balancing network (FMFN-YOLO), which is based on the YOLO architecture. Firstly, we investigated the impact of various scale detection heads on model performance. Based on our findings, we readjusted the network structure, thereby enhancing the model’s ability to detect dense objects. Secondly, we proposed three innovative plug-and-play modules: the fine-grained preservation feature aggregation module (FPFA), the multi-scale feature enhancement module (MFEM), and the feature pyramid balancing module (FPBM). These three modules are respectively used to enhance the network’s capabilities in fine-grained information retention, multi-scale feature extraction, and multi-level feature fusion, while effectively suppressing interference from complex backgrounds. We demonstrate the effectiveness of our method on the VisDrone2019 and AI-TOD datasets, where FMFN-YOLO achieves a mAP50 of 44.5 and 43.6%, respectively. This represents an improvement of 7.6 and 6.4% over the baseline, respectively. Similarly, it offers significant advantages over state-of-the-art methods.