基于YOLOv5的轻量级无人机检测算法
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TP391

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军队装备综合研究项目(WJ20211A030131);国防科技创新自主选题项目(ZZKY20223105),


Lightweight UAV Detection Algorithm based on YOLOv5
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    摘要:

    为解决无人机“黑飞”造成的安全隐患,针对现有的基于深度学习的无人机目标检测算法模型参数量大、训练耗时长的问题,提出了一种基于YOLOv5算法的轻量级无人机实时目标检测(ED-YOLOv5s)算法。首先结合轻量化模型EMO对YOLOv5骨干网络的特征提取部分进行重构;其次引入归一化高斯Wasserstein距离(normalized gaussian wasserstein distance,NWD)和比例因子计算候选框之间的相似度来部分替代IoU(Intersectiom over Union);然后引入无参注意力机制SimAM以优化权重分布,提升检测精确度,以达到对YOLOv5网络的Backbone、Head进行优化的效果,最终得到模型大小为4.57 M,浮点运算量为26.1 GFLOPs的ED-YOLOv5s轻量级无人机检测算法。实验数据表明,改进后的算法提高了检测精度,实现了模型轻量化,所提算法在DUT Anti-UAV数据集上AP@50值达到96.7%,在RTX2060显卡上检测速度达到37 frame/s。

    Abstract:

    [Abstract] In order to solve the security risks caused by UAV "black flight", aiming at the problems of large model parameters and long training time of the existing UAV target detection algorithm based on deep learning, a lightweight real-time object detection algorithm for UAV based on YOLOv5 (ED-YOLOv5s) is proposed. Firstly, the feature extraction part of YOLOv5 backbone network was reconstructed by combining the lightweight model EMO. Secondly, Normalized Gaussian Wasserstein Distance (NWD) and scale factor were introduced to calculate the similarity between candidate boxes to partially replace IoU. Then, SimAM was introduced to optimize the weight distribution and improved the detection accuracy, so as to achieve the effect of optimizing the Backbone and Head of YOLOv5 network. Finally the model size was 4.57 M. ED-YOLOv5s lightweight UAV detection algorithm with floating point computation of 26.1(GFLOPs). The experimental data showed that the improved algorithm improved the detection accuracy and realized the model lightweight. The proposed algorithm achieved 96.7% AP@50 value on the DUT Anti-UAV dataset, and the detection speed reached 37frame/s on the RTX2060 GPU.

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郝鹤翔,彭月平,韩佰轩,等. 基于YOLOv5的轻量级无人机检测算法[J]. 科学技术与工程, 2024, 24(28): 12251-12258.
Hao Hexiang, Peng Yueping, Han Baixuan, et al. Lightweight UAV Detection Algorithm based on YOLOv5[J]. Science Technology and Engineering,2024,24(28):12251-12258.

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  • 收稿日期:2024-02-01
  • 最后修改日期:2024-08-03
  • 录用日期:2024-04-25
  • 在线发布日期: 2024-11-05
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