Multi-Scale Detection Method for Soldier and Armored Vehicle Objects

被引：0

作者：

Wang J. ^{[1
]}

Yu Z. ^{[1
]}

Wang H. ^{[1
]}

机构：

[1] School of Mechatronical Engineering, Beijing Institute of Technology, Beijing

来源：

Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2023年 / 43卷 / 02期

关键词：

data augmentation; mutil-scale object detection; small object detection;

D O I：

10.15918/j.tbit1001-0645.2022.022

中图分类号：

学科分类号：

摘要：

A multi-scale object detection method was proposed based on YOLOv4 deep learning algorithm to solve the multi-scale problem caused by the huge-scale difference between soldiers and armored vehicles, as well as object distance. The diversity of small object samples was enriched through targeted data augmentation methods input images were segmented to improve the resolution of input small objects of network, the detection results of large, medium and small objects were separated based on the feature pyramid network, and finally the detection results were matched and NMS processing was carried out to remove the redundant detection boxes, so as to achieve multi-scale object detection. The experimental results show that the average mean precision of small and medium objects is improved by 1.20% and 5.54% respectively, while the detection effect of large objects is maintained, which effectively improves the detection effect of small and medium objects. © 2023 Beijing Institute of Technology. All rights reserved.

引用

页码：203 / 212

页数：9

共 16 条

[1] ZHOU Beibei, LIU Jue, Application of intelligent technology in precision strike system, Air& Space Defense, 2, 3, pp. 77-83, (2019)
[2] DENG Chunfang, Multi-scale object detection based on deep learning, (2021)
[3] CHEN Keqi, ZHU Zhiliang, DENG Xiaoming, Et al., Deep learning for multi-scale object detection: a survey, Journal of Software, 32, 4, pp. 1201-1227, (2021)
[4] SINGH B, DAVIS L., An analysis of scale invariance in object detection - SNIP, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3578-3587, (2018)
[5] SINGH B, NAJIBI M, DAVIS L., SNIPER: efficient multi-scale training, Advances in Neural Information Processing Systems, 31, 15, pp. 9310-9321, (2018)
[6] MENG Fanjie, WANG Xinqing, SHAO Faming, Et al., Fast-armored target detection based on multi-scale representation and guided anchor, Defence Technology, 16, 4, pp. 922-932, (2020)
[7] LIN T Y, DOLLAR P, GIRSHICK R, Et al., Feature pyramid networks for object detection, Journal of Visual Communication and Image Representation, 79, (2016)
[8] LIU W, ANGUELOV D, ERHAN D, Et al., SSD: single shot multibox detector, Proceedings of European Conference on Computer Vision, pp. 21-37, (2016)
[9] SUN Haoze, CHANG Tianqing, WANG Quandong, Et al., Image detection method for tank and armored targets based on hierarchical multi-scale convolution feature extraction, Acta Armamentarii, 38, 9, pp. 1681-1691, (2017)
[10] ZHOU Zhiguo, LIU Kaiyuan, ZHENG Yipeng, Et al., A real-time algorithm for visual detection of high-speed unmanned surface vehicle based on deep learning, Transactions of Beijing Institute of Technology, 41, 7, pp. 758-764, (2021)

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