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<p>Detecting small targets in UAV remote sensing images is challenging for traditional lightweight methods due to difficulty in feature extraction and high background interference. We propose LPS-YOLO, which improves small target feature extraction while reducing computational complexity by replacing the Conv backbone with SPDConv to retain fine-grained features. LPS-YOLO introduces the SKAPP module for better feature fusion and incorporates the E-BiFPN and OFTP structures to efficiently preserve and transfer backbone information. Evaluation of the VisDrone2019 dataset shows a 17.3% increase in mean Average Precision (mAP) and a 42.5% reduction in parameters compared to the baseline. Additional experiments on the DOTAv2 dataset demonstrate the model’s robustness, with a 14.5% improvement in F1 score and a 14.9% increase in mAP over YOLOv8-n. LPS-YOLO offers an effective solution for multi-target detection in UAVs.</p>

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Lightweight multidimensional feature enhancement algorithm LPS-YOLO for UAV remote sensing target detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月8日

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  • Yong Lu,
  • Minghao Sun

摘要

Detecting small targets in UAV remote sensing images is challenging for traditional lightweight methods due to difficulty in feature extraction and high background interference. We propose LPS-YOLO, which improves small target feature extraction while reducing computational complexity by replacing the Conv backbone with SPDConv to retain fine-grained features. LPS-YOLO introduces the SKAPP module for better feature fusion and incorporates the E-BiFPN and OFTP structures to efficiently preserve and transfer backbone information. Evaluation of the VisDrone2019 dataset shows a 17.3% increase in mean Average Precision (mAP) and a 42.5% reduction in parameters compared to the baseline. Additional experiments on the DOTAv2 dataset demonstrate the model’s robustness, with a 14.5% improvement in F1 score and a 14.9% increase in mAP over YOLOv8-n. LPS-YOLO offers an effective solution for multi-target detection in UAVs.