摘要
针对靶场返回舱自动识别与跟踪的需求,采用YOLOv5神经网络进行自动识别的技术方案。为解决返回舱数据集较少的问题,采用Mosaic数据增强的方法,不仅增加了数据集数量,而且提高了网络的鲁棒性。针对返回舱目标较小的问题,提出以特征明显、目标较大的降落伞作为主要识别对象,返回舱本体为次要识别对象,对二者同时进行识别。通过数据集的建立、训练和测试,最终得出实验结论:在不同环境、不同光线条件下的降落伞和返回舱识别中,所提方法检测准确率可达90%~95%,对返回舱落地过程中高效、准确识别跟踪任务具有重要意义。
Aiming at the requirement of automatic identification and tracking of the return cabin in the shooting range,it adopts YOLOv5 neural network for automatic identification.In order to solve the problem of less data sets in the return cabin,Mosaic data enhancement method is adopted,which not only increases the number of data sets,but also improves the robustness of the network.Aiming at the problem that the target of the return cabin is small,it is proposed,taking the parachute with obvious characteristics and large target as the main identification object and the return cabin body as the secondary identification object,to identify both of them at the same time.Through the establishment,training and testing of the data sets,the experimental conclusion is finally drawn:in the identification of parachutes and return cabins under different environments and different light conditions,the detection accuracy of the proposed method can reach 90%~95%,which is of great significance to identify tracking targets efficiently and accurately during the landing of return cabins.
作者
冯凯
张书雅
李锦暄
马淑丽
钱克昌
FENG Kai;ZHANG Shuya;LI Jinxuan;MA Shuli;QIAN Kechang(School of Aerospace Command,PLA Strategic Support Force University of Aerospace Engineering,Beijing 101416,China;School of Aerospace Information,PLA Strategic Support Force University of Aerospace Engineering,Beijing 101416,China)
出处
《现代信息科技》
2021年第10期20-26,共7页
Modern Information Technology
作者简介
冯凯(2001-),男,汉族,山西临汾人,本科在读,研究方向:目标检测算法;张书雅(2001-),女,汉族,陕西宝鸡人,本科在读,研究方向:目标检测算法;李锦暄(2001-),女,汉族,陕西宝鸡人,本科在读,研究方向:目标检测算法;马淑丽(1989-),女,汉族,山东济宁人,讲师,博士,研究方向:图像重建、凸优化、张量理论;钱克昌(1984-),男,汉族,江苏邳州人,副教授,博士,研究方向:信息对抗与安全。
