Military Target Detection Method Based on Improved YOLOv5

Xiuli Du, Li-quan Song, Yana Lv, Xutong Qin
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Abstract

Aiming at the requirement of military target detection under the condition of limited resources of weapon hardware platform, this paper proposes a military target detection method that takes into account network lightweight, mean average precision (mAP) and detection speed. This method is based on the You Only Look Once Version 5 (YOLOv5) algorithm. First, the Stem block module is used to replace the Focus module, which can effectively improve the feature expression ability and reduce the amount of parameters and computation of the network model. Second, a MobileNetV2-Convolutional Block Attention Module (MNtV2-CBAM) structure is designed with MobileNetV2 integrated into the CBAM mechanism. The amount of network parameters and computation is reduced, while the detection performance of the model is improved. The experimental results show that compared with the YOLOv5 algorithm, the mAP value of the method in this paper is increased by 1.3%, and the amount of parameters and the amount of calculation are decreased by 67.45% and 73.17% respectively, which can be better applied to the resource-constrained weapon equipment platform. In this way, the reconnaissance and analysis capabilities of military intelligence can be improved, the decision-making time of the commander can be shortened, and the combat capability of the troops can be greatly improved.
基于改进YOLOv5的军事目标检测方法
针对武器硬件平台资源有限条件下的军事目标检测需求,提出了一种兼顾网络轻量化、平均精度(mAP)和检测速度的军事目标检测方法。该方法基于You Only Look Once Version 5 (YOLOv5)算法。首先,采用Stem块模块代替Focus模块,有效提高了特征表达能力,减少了网络模型的参数量和计算量。其次,将MobileNetV2集成到CBAM机制中,设计了MobileNetV2-卷积块注意模块(MNtV2-CBAM)结构。减少了网络参数和计算量,提高了模型的检测性能。实验结果表明,与YOLOv5算法相比,本文方法的mAP值提高了1.3%,参数量和计算量分别减少了67.45%和73.17%,能够更好地应用于资源受限的武器装备平台。这样可以提高军事情报的侦察和分析能力,缩短指挥员的决策时间,大大提高部队的作战能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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