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无人机作战平台的智能目标识别方法

Intelligent Target Recognition Method of Unmanned Aerial Vehicle Combat Platform

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摘要

深入研究了流行的目标识别方法YOLOv3,将Inception模块融入其特征提取网络darknet-53中,从而得到新网络darknet-139。相比YOLOv3特征提取网络,新网络具有更好的特征提取能力。采集并制作算法所需的数据集,分别在YOLOv3和本文算法上进行训练并测试。实验结果表明,相比YOLOv3,本文算法的平均识别率提升了约2%。

Abstract

The popular target recognition method YOLOv3 is deeply studied, and the Inception module is integrated into the feature extraction network darknet-53 to get a new network darknet-139. Compared with YOLOv3, the new network has better ability in feature extraction. The data set required by the algorithm is collected and made, and were trained and tested on YOLOv3 and the proposed algorithm, respectively. The experimental results show that the average recognition rate of the proposed algorithm is about 2% higher than that of YOLOv3.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.7

DOI:10.3788/lop56.071001

所属栏目:图像处理

基金项目:陆军装备部“十三五”预研基金

收稿日期:2018-09-17

修改稿日期:2018-10-17

网络出版日期:2018-10-22

作者单位    点击查看

吕攀飞:陆军炮兵防空兵学院, 安徽 合肥 230031
王曙光:陆军炮兵防空兵学院, 安徽 合肥 230031

联系人作者:吕攀飞(1055392772@qq.com)

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引用该论文

Lü Panfei,Wang Shuguang. Intelligent Target Recognition Method of Unmanned Aerial Vehicle Combat Platform[J]. Laser & Optoelectronics Progress, 2019, 56(7): 071001

吕攀飞,王曙光. 无人机作战平台的智能目标识别方法[J]. 激光与光电子学进展, 2019, 56(7): 071001

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