Armored Vehicle Recognition Method Integrating Attention and Path Aggregation Mechanism

CONG  Yuhua; WANG  Zhisheng; XING  Changda

doi:10.15892/j.cnki.djzdxb.2021.05.027

Journal of Projectiles, Rockets, Missiles and Guidance >

2021 , Vol. 41 >Issue 5: 138 - 144

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2021.05.027

Armored Vehicle Recognition Method Integrating Attention and Path Aggregation Mechanism

CONG Yuhua ,
WANG Zhisheng ,
XING Changda

Expand

¹ Nanjing University of Science and Technology ZiJin College, Nanjing 210023, China
² College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
³ Shenzhen Research Institute of Nanjing University of Aeronautics and Astronautics, Guangdong Shenzhen 518063, China

Received date: 2021-07-27

Online published: 2025-05-30

Fold

Abstract

Yolov4Tiny based on convolutional neural network is suitable for the recognition of armored vehicles in battlefield environment because of its lightweight and high speed, but it exchanges speed with loss of accuracy. Therefore, it needs to be improved on the basis of ensuring a certain recognition speed. Firstly, the attention mechanism is introduced into the backbone network to strengthen the key feature extraction in channel and space. Then the path aggregation method is introduced, and the bottomup path aggregation method is integrated into the feature pyramid structure to strengthen the extraction of different scale features. Through the comparison of the effects of five network structures on armored vehicle recognition, the improved network has fast realtime speed and greatly improved accuracy on the basis of ensuring lightweight characteristics, which proves the effectiveness of the improvement.

Key words： armored car; target identification; Yolov4-Tiny; attention mechanism; path aggregation mechanism

Cite this article

CONG Yuhua , WANG Zhisheng , XING Changda . Armored Vehicle Recognition Method Integrating Attention and Path Aggregation Mechanism[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2021 , 41(5) : 138 -144 . DOI: 10.15892/j.cnki.djzdxb.2021.05.027

[an error occurred while processing this directive]

References

[1]	刘英 . 基于卷积神经网络的陆战场目标分类算法研究 [D]. 成都 : 电子科技大学 , 2020 .
[2]	程学生 , 姚旺生 . 一种坦克装甲车识别方法 [J]. 电子测量技术 , 2007,30 (12): 57 - 58 .
[3]	顾超越 , 李喆 , 史晋涛 , 等 . 基于改进 Faster-RCNN 的无人机巡检架空线路销钉缺陷检测 [J]. 高电压技术 , 2020(9): 3089 - 3096 .
[4]	REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: unified, real-time object detection [C]// Anon. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016:779 - 788 .
[5]	REDMON J , FARHADIA A . YOLO9000: better, faster, stronger [C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017:7263 - 7271 .
[6]	REDMON J , FARHADI A . Yolov3: an incremental improvement [J]. ArXiv E-prints , 2018(4): 1 - 5 .
[7]	BOCHKOVSKIY A , WANG CY , LIAO HY . YOLOV4: Optimal speed and accuracy of object detection [J]. Computer Vision and Pattern Recognition , 2020,17 (9): 198 - 215 .
[8]	郭紫嫣 , 韩慧妍 , 何黎刚 , 等 . 基于改进的YOLOV4的手势识别算法及其应用 [J]. 中北大学学报 , 2021,42 (3): 223 - 231 .
[9]	马超杰 , 杨华 , 吴丹 , 等 . 自动目标识别技术在武器系统中的应用 [J]. 飞航导弹 , 2008(10): 45 - 48 .
[10]	CHUA L O , ROSKA T . The CNN paradigm [J]. IEEE Transactions on Circuits and Systems I-regular Papers , 1993,40 (3): 147 - 156 .
[11]	KRIZHEVSKY A , SUTSKEVER I , HINTON G E . Imagenet classification with deep convolutional neural networks [J]. Communications of the ACM , 2017,60 (6): 84 - 90 .
[12]	OQUAB M , BOTTOU L , LAPTEV I , et al . Learning and transferring mid-level image representations using convolutional neural networks [C]// IEEE. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition , 2014:1717 - 1724 .
[13]	GOODFELLOW I , BENGIO Y , COURVILLE A . Deep learning [M]. Cambridge : MIT Press , 2016:326 - 366 .
[14]	BRUNA J , SZLAM A , LECUN Y . Signal recovery from pooling representations [J]. Statistics , 2014,32 . 307 - 315 .
[15]	WOO S , PARK J , LEE JY , et al . CBAM: Convolutional block attention module [C]// ECCV. Proceedings of the 2018 European Conference on Computer Vision , 2018:1 - 17 .
[16]	LIU S , QI L , QIN H , et al . Path aggregation network for instance segmentation [C]// IEEE. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018:1 - 11 .

Options

Outlines