复杂战场环境下改进YOLOv5军事目标识别算法研究

Research on Improved YOLOv5-based Military Target Recognition Algorithm Used in Complex Battlefield Environment

复杂战场环境下军事目标识别技术是提升战场情报获取能力的基础和关键。针对当前军事目标识别技术在复杂战场环境下漏检误检率高、实时性差等问题,提出一种基于改进YOLOv5模型的PB-YOLO军事目标识别算法。将改进的目标识别算法对于陆战场军事单元的识别锚框进行重新聚类,以提升模型对于目标大小适应度,加速模型收敛;采用通道-空间并行注意力机制,增加模型对复杂战场环境下目标特征信息与位置信息关注度;在特征融合网络部分使用BiFPN以提升模型对于特征的融合能力与速度;采用Alpha_IoU损失函数加速模型收敛,解决当真实框与预测框重合时IoU计算退化问题。实验结果表明,在自建军事目标数据集下,改进算法与主流目标识别算法相比,在保证模型空间复杂度的同时,mAP值达到了90.17%。消融实验对比结果表明,改进后网络较原模型精度提升11.57%,具有较好的识别性能,能够为战场情报获取提供有效的技术支撑。

Military target recognition technology used in complex battlefield environment is the basis and key to improve the battlefield intelligence acquisition capability.A PB-YOLO military target recognition algorithm based on the improved YOLOv5 model is proposed to solve the problems of high missed and false detection rates and poor real-time performance of current military target recognition technology in complex battlefield environments.The improved target recognition algorithm is re-clustered for the identification anchor boxes of military units in the land battlefield to improve the model s fitness for target size and accelerate the convergence of model,and the channel-spatial parallel attention mechanism is used to increase the model s attention to the feature information and location information of the targets in complex battlefield environments.BiFPN is used in the feature fusion network part to improve the fusion ability and speed of the model for features,and the Alpha_IoU loss function is used to accelerate the convergence of model,and solve the problem of IoU calculation degradation when the real frame and the predicted frame overlap.The experimental results show that,compared with the mainstream target recognition algorithm,the mAP value obtained by the improved algorithm reaches 90.17%while ensuring the model space complexity under the self-built military target data set.,Through the comparison of ablation experiments,the results show that,compared with the original model,the accuracy of the improved network is improved by 11.57%,and it has better recognition performance,which can provide effective technical support for battlefield intelligence acquisition.