摘要
导弹末制导飞行过程中,基于传统方法检测红外目标时准确率和实时性不足。针对这一问题,提出一种基于改进YOLO v3的红外末制导目标检测方法。从红外末制导背景出发,优化损失权重,提高了网络定位和分类能力。充分利用Adam算法自适应和动量法稳定的特点,运用"预训练"的思想,提出一种联合训练的方法,大幅提高模型检测精度。实验表明,改进算法在设计的红外目标数据集上进行训练和测试,检测效果理想,平均准确率达到77.89%,检测速度达到25frame/s,虚警率和漏检率都得到有效降低。
The traditional infrared target detection method for missile homing guidance is flawed because of low accuracy and lack of real-time feedback.Therefore,an infrared homing guidance target detection method based on the improved YOLO v3 is proposed,and it involves the optimization of the weight loss by considering the background of infrared homing guidance,improving the accuracy of positioning and classification.Subsequently,the adaptive moment estimation(Adam)and stable stochastic gradient descent(SGD)with momentum are fully exploited.Further,ajoint training method predicated on pre-training,which significantly improves the accuracy of detection,is proposed herein.The improved algorithm is ideally trained and tested on the infrared target dataset designed in this work.The best mean average precision is 77.89%,and all the detection rates are greater than25 frame/s.The false and missing alarm probabilities are effectively reduced.
作者
陈铁明
付光远
李诗怡
李源
Chen Tieming;Fu Guangyuan;Li Shiyi;Li Yuan(Department of Information Engineering,Rocket Force University of Engineering,Xi'an,Shaanxi710025,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第16期147-154,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61403397,61503389,61202332)
中国博士后科学基金(2012M521905)
陕西省自然科学基础研究计划(2015JM6313)
