A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets

ZHENG Lu; PENG Yueping; ZHOU Tongtong

Volume 45 Issue 5

May 2023

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ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.

Citation:

ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.

ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.

Citation:

ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.

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A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets

ZHENG Lu^{1, 2
,},
PENG Yueping^{2
,
,},
ZHOU Tongtong^{1, 2}

1.
Postgraduate Brigade, Engineering University of PAP, Xi'an 710086, China
2.
College of Information Engineering, Engineering University of PAP, Xi'an 710086, China

Received Date: 2022-06-05
Rev Recd Date: 2022-06-23
Publish Date: 2023-05-20

Abstract

Abstract

To solve the problems of large parameters, high complexity, and poor detection performance of multiscale targets in the existing infrared target detection algorithms based on deep learning, a lightweight infrared target detection algorithm for multiscale targets is proposed. Based on YOLOv3, the algorithm uses the MobileNet V2 backbone network, simplified spatial pyramid structure (simSPP), anchor-free mechanism, decoupling head, and simplified positive and negative sample allocation strategies (SimOTA) to optimize the backbone, neck, and head, respectively. Finally, LMD-YOLOv3 with the model size of 6.25 M and floating-point computation of 2.14 GFLOPs was obtained. Based on the MTS-UAV data set, the mAP reached 90.5%, and on the RTX2080Ti dataset, the FPS reached 99. Compared with YOLOv3, mAP increased by 11.7%, and the model size was only 1/10 of YOLOv3.
- object detection,
- multi-scale,
- lightweight algorithm

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References(17)

References

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