基于改进的YOLOv5雷达RD图像目标识别算法
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作者单位:

1.火箭军工程大学;2.火箭军工程大学 导弹工程学院

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中图分类号:

TP391??

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Improved Radar RD Image Target Detection Algorithm Based on YOLOv5
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    目前,在强干扰条件下,雷达舰船目标识别变得更加困难,为此,本文提出了一种基于YOLOv5模型的改进模型以提升对舰船目标和无源干扰的检测精度和适应性。首先,通过在YOLOv5模型中增加针对小目标的检测层,优化了原始网络,其目的是增强对小目标的检测准确性,确保在复杂海洋环境中更有效地识别目标舰船;其次,由于集成了高效多尺度注意力机制(Exponential Moving Average,EMA),不仅可以减轻海洋噪声和复杂背景的影响,还可以使得模型在识别中具有更强的特征表达能力,从而提高算法识别性能。最后,利用无源干扰环境下的舰船RD图像数据集进行了实验验证,实验结果表明本文提出的改进方法在RD数据集上取得了较好的目标识别性能,有效提高了目标识别的准确度。

    Abstract:

    At present, it is more difficult to recognize ship targets under strong jamming conditions. Therefore, an improved model based on YOLOv5 model is proposed in this paper to improve the detection accuracy and adaptability of ship targets and passive jamming. First, the original network is optimized by adding a detection layer for small targets in the YOLOv5 model, which aims to enhance the detection accuracy of small targets and ensure more effective identification of target ships in complex Marine environments. Secondly, due to the integration of an Exponential Moving Average (EMA), not only can reduce the impact of ocean noise and complex background, but also make the model have stronger feature expression ability in recognition, thus improving the recognition performance of the algorithm. Finally, the ship RD image data set under passive interference environment is used for experimental verification. The experimental results show that the improved method in this paper achieves better target recognition performance on the RD data set, and effectively improves the accuracy of target recognition.

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  • 收稿日期:2024-05-27
  • 最后修改日期:2024-05-27
  • 录用日期:2024-06-03
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