Fast Armored Target Detection Based on Lightweight Network

被引：0
|
作者：
Sun H. [1 ,2 ]
Chang T. [1 ]
Zhang L. [1 ]
Yang G. [1 ]
Han B. [1 ]
Li Y. [3 ]
机构：
[1] Department of Weapon and Control, Army Academy of Armored Forces, Beijing
[2] 78123 Troop of the PLA, Chengdu
[3] The 2rd District, Army Base of Test and Training, Weinan
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 07期
关键词：
Armored target; Lightweight convolutional neural network; One-stage detector; Target detection;
D O I：
10.3724/SP.J.1089.2019.17467
中图分类号：
学科分类号：
摘要：
Focused on the detection task of armored target in battlefield environment, a fast detection method based on lightweight convolutional neural network is proposed in this paper. Firstly, based on the lightweight backbone network (MobileNet), a multi-scale single-stage detection framework is developed. Secondly, considering the size distribution of armored target, higher resolution feature maps are selected and a new designed Resblock is added to each detection unit to enhance the detection performance for small targets. At last, focal-loss function is introduced to replace the traditional cross entropy loss function, which effectively overcomes the extreme imbalance of the distribution of the positive and negative samples in training processes. A special detection dataset for armored target is constructed, based on which the comparable experiments with state-of-art detection methods are conducted. Experimental results show that the proposed method achieves good performance in detection accuracy, model size and operation speed, and is especially suitable for small mobile reconnaissance platforms such as UAVs (unmanned aerial vehicle). © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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收藏
页码：1110 / 1121
页数：11
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