智能靶标目标检测方法研究

在激光模拟训练中,具有智能打击功能的靶标装置可以更加逼真地模拟实战对抗过程,满足智能化军事训练需求。但目前大多数靶标不具备智能打击或反击功能,靶标仅能实现作为目标的被打击功能或者非智能的触发反击功能,模拟训练形式单一,靶标运动或打击功能呈现规律性,不能真实模拟敌方目标主动攻击的能力,因此为适应模拟训练发展的需求实现模拟训练智能化、提高模拟仿真程度就需要通过智能靶标来模拟敌方目标进行作战。在智能靶标的研制过程中其中首先要解决的问题就是目标检测。本研究最终目标是对战场环境中的单兵目标进行检测并做出攻击活动,故对单兵目标的检测速度和精度有较大要求,本文提出一种基于Yolov5神经网络架构提高目标检测精度、加快目标检测速度的算法,采用改进网络结构对传统的目标检测算法进行优化,保证在输入可见光图像下,能够实现快速检测,并保证检测的精度。

Research on intelligent target object detection methods

In laser simulation training,target devices with intelligent strike functions can more realistically simulate the actual combat confrontation process to meet the needs of intelligent military training.However,most of the current targets do not have intelligent strike or counterattack functions,targets can only achieve the target hit function or non intelligent trigger counterattack function,the target can only realize the function of being struck as a target or non intelligent trigger counterattack function.The simulation training format is monolithic,with target movement or strike function showing regularity,and does not realistically simulate the ability of an enemy target to initiate an attack.Therefore,in order to adapt to the needs of simulation training,it is necessary to simulate the enemy targets through intelligent targets for combat.In the development process of intelligent targets,one of the first problems to be solved is object detection.The ultimate goal of this research is to detect and make attack activities on individual targets in the battlefield environment,so the detection speed and accuracy of individual targets have greater requirements.In this paper,an algorithm based on Yolov5 neural network architecture to improve the accuracy of target detection and accelerate the speed of target detectionand to optimize the traditional target detection algorithm is proposed using theimproved the network structure to ensure that under the input visible light image,it can achieve rapid detection and ensure the accuracy of detection.