一种改进YOLOv5算法的伪装目标检测方法
A Camouflaged Target Detection Method with Improved YOLOv5 Algorithm
彭锐晖 1赖杰 2孙殿星 3李莽 2颜如玉 2李雪2
作者信息
- 1. 哈尔滨工程大学信息与通信工程学院 哈尔滨 150001;哈尔滨工程大学青岛创新发展基地 青岛 266000
- 2. 哈尔滨工程大学青岛创新发展基地 青岛 266000
- 3. 哈尔滨工程大学青岛创新发展基地 青岛 266000;海军航空大学信息融合研究所 烟台 264001
- 折叠
摘要
为了深入挖掘伪装目标特征信息含量、充分发挥目标检测算法潜能,解决伪装目标检测精度低、漏检率高等问题,该文提出一种多模态图像特征级融合的伪装目标检测算法(CAFM-YOLOv5).首先,构建伪装目标多波谱数据集用于多模态图像融合方法性能验证;其次,构建双流卷积通道用于可见光和红外图像特征提取;最后,基于通道注意力机制和空间注意力机制提出一种交叉注意力融合模块,以实现两种不同特征有效融合.实验结果表明,模型的检测精度达到96.4%、识别概率88.1%,优于YOLOv5参考网络;同时,在与YOLOv8等单模态检测算法、SLBAF-Net等多模态检测算法比较过程中,该算法在检测精度等指标上也体现出巨大优势.可见该方法对于战场军事目标检测具有实际应用价值,能够有效提升战场态势信息感知能力.
Abstract
To comprehensively explore the information content of camouflaged target features,leverage the potential of target detection algorithms,and address issues such as low camouflage target detection accuracy and high false positive rates,a camouflage target detection algorithm named CAFM-YOLOv5(Cross Attention Fusion Module Based on YOLOv5)is proposed.Firstly,a camouflaged target multispectral dataset is constructed for the performance validation of the multimodal image fusion method;secondly,a dual-stream convolution channel is constructed for visible and infrared image feature extraction;and finally,a cross-attention fusion module is proposed based on the channel-attention mechanism and spatial-attention mechanism in order to realise the effective fusion of two different features.Experimental results demonstrate that the model achieves a detection accuracy of 96.4%and a recognition probability of 88.1%,surpassing the YOLOv5 baseline network.Moreover,when compared with unimodal detection algorithms like YOLOv8 and multimodal detection algorithms such as SLBAF-Net,the proposed algorithm exhibits superior performance in detection accuracy metrics.These findings highlight the practical value of the proposed method for military target detection on the battlefield,enhancing situational awareness capabilities significantly.
关键词
伪装目标检测/多波谱数据集/注意力机制/可见光图像/红外图像Key words
Camouflaged target detection/Multispectral datasets/Attention mechanisms/Visible images/Infrared images引用本文复制引用
基金项目
航天科技集团稳定支持项目(ZY0110020009)
国防科技重点实验室基金项目(2023-JCJQ-LB-016)
出版年
2024
NETL
NSTL
万方数据