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<p>In aerial target detection tasks, achieving a balance between high detection accuracy and low computational cost remains a key challenge. To address the aforementioned problems, this paper introduces the lightweight multiscale feature fusion and attention-YOLO (LMSFA-YOLO), which is lightweight and accurate for aerial tiny target recognition. Firstly, we propose a lightweight multiscale convolution (LMSConv) and a lightweight multiscale cross-stage partial (LMSCSP). These methods optimize convolutional computation cost and enhance multiscale information extraction, significantly reducing computational cost and parameters, while improving feature representation and fusion without sacrificing accuracy. Subsequently, the mixed local channel attention (MLCA) is combined to create an effective mixed channel attention spatial pyramid pooling (EMCASPP), aiming to simultaneously integrate local and channel space information to enhance the feature fusion ability of the model. To further improve the precision of feature extraction and preserve detailed information, a high-resolution shallow feature layer is applied. Finally, to increase the accuracy of bounding box regression, we introduce ShapeIoU to emphasize the scale and shape of the bounding box, replacing the original IoU. Experimental results demonstrate that LMSFA-YOLO surpasses the baseline YOLOv5s with 35.8% fewer parameters while improving the F1-score and mAP by 5.0% and 7.0% on the VisDrone dataset and by 6.1% and 7.6% on the AI-TOD dataset. Furthermore, it runs in real-time (&gt; 30 FPS) on the Jetson Orin Nano. These results validate the effectiveness of LMSFA-YOLO in achieving high detection performance while reducing computation cost, making it well-suited for deployment on edge devices with limited computational resources.</p>

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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月10日

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  • Yuanbo Chu,
  • Jiahao Wang,
  • Longhui Ma,
  • Chenxing Wu

摘要

In aerial target detection tasks, achieving a balance between high detection accuracy and low computational cost remains a key challenge. To address the aforementioned problems, this paper introduces the lightweight multiscale feature fusion and attention-YOLO (LMSFA-YOLO), which is lightweight and accurate for aerial tiny target recognition. Firstly, we propose a lightweight multiscale convolution (LMSConv) and a lightweight multiscale cross-stage partial (LMSCSP). These methods optimize convolutional computation cost and enhance multiscale information extraction, significantly reducing computational cost and parameters, while improving feature representation and fusion without sacrificing accuracy. Subsequently, the mixed local channel attention (MLCA) is combined to create an effective mixed channel attention spatial pyramid pooling (EMCASPP), aiming to simultaneously integrate local and channel space information to enhance the feature fusion ability of the model. To further improve the precision of feature extraction and preserve detailed information, a high-resolution shallow feature layer is applied. Finally, to increase the accuracy of bounding box regression, we introduce ShapeIoU to emphasize the scale and shape of the bounding box, replacing the original IoU. Experimental results demonstrate that LMSFA-YOLO surpasses the baseline YOLOv5s with 35.8% fewer parameters while improving the F1-score and mAP by 5.0% and 7.0% on the VisDrone dataset and by 6.1% and 7.6% on the AI-TOD dataset. Furthermore, it runs in real-time (> 30 FPS) on the Jetson Orin Nano. These results validate the effectiveness of LMSFA-YOLO in achieving high detection performance while reducing computation cost, making it well-suited for deployment on edge devices with limited computational resources.