基于深度学习的红外舰船目标识别  

Target Recognition of Infrared Ship Based on Deep Learning

作　　者：杨涛 戴军[1] 吴钟建[1] 金代中[1] 周国家 YANG Tao;DAI Jun;WU Zhongjian;JIN Daizhong;ZHOU Guojia(Institute of Southwest Technical Physics,Chengdu 610041,China)

机构地区：[1]西南技术物理研究所,四川成都610041

出　　处：《红外技术》2020年第5期426-433,共8页Infrared Technology

摘　　要：本文采用深度学习技术中的YOLOv3(You Only Look Once Version 3)目标识别算法对红外成像仪从海面采集的红外图像中舰船进行识别。红外成像仪采集图像的频率高达50帧/s,为了能减少网络计算时间,本文借鉴YOLOv3的一些思想,采用全卷积结构和LeakReLU激活函数重新设计一个轻量化的基础网络,以此加快检测速度。输出层根据采集回来的红外图像的特点采用Softmax算法回归,在提高检测速度的同时,也兼顾了检测精度。In this study,the You Only Look Once Version 3(YOLOv3)target recognition algorithm in deep learning technology is used to identify the ship in an infrared image collected using an infrared imager from the sea surface.The infrared imager captures images at a frequency of up to 50 frames per second.To reduce network computing time,a few ideas are generated based on YOLOv3;additionally,a full convolution structure and the LeakReLU activation function are used to redesign a lightweight basic network to accelerate detection.The output layer uses the softmax algorithm to regress according to the characteristics of the collected infrared images,which improves the detection speed and accounts for detection accuracy.

关 键 词：红外图像 目标识别 深度学习 YOLOv3 

分 类 号：TN957.52[电子电信—信号与信息处理] TP18[电子电信—信息与通信工程]