Feb. 2023

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Article Navigation> Transactions of Beijing institute of Technology> 2023> 43(2): 203-212

WANG Jianzhong, WANG Jiale, YU Zibo, WANG Hongfeng. Multi-Scale Detection Method for Soldier and Armored Vehicle Objects[J]. Transactions of Beijing institute of Technology, 2023, 43(2): 203-212. doi: 10.15918/j.tbit1001-0645.2022.022

Citation:

WANG Jianzhong, WANG Jiale, YU Zibo, WANG Hongfeng. Multi-Scale Detection Method for Soldier and Armored Vehicle Objects[J].Transactions of Beijing institute of Technology, 2023, 43(2): 203-212.doi:10.15918/j.tbit1001-0645.2022.022

Citation:

WANG Jianzhong, WANG Jiale, YU Zibo, WANG Hongfeng. Multi-Scale Detection Method for Soldier and Armored Vehicle Objects[J].Transactions of Beijing institute of Technology, 2023, 43(2): 203-212.doi:10.15918/j.tbit1001-0645.2022.022

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Multi-Scale Detection Method for Soldier and Armored Vehicle Objects

doi:10.15918/j.tbit1001-0645.2022.022

School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

Received Date:2022-06-18
Accepted Date:2022-08-30

Abstract

Abstract

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.
- mutil-scale object detection,
- small object detection,
- data augmentation

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References (16)

References

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