Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Yuanxue Ding; Yanchen Qu; Di Liu; Xin Zhou; Pengfei Wang; Ruiqi Zhou; Zhihui Liu; Yuebing Zhu

doi:10.1117/12.3021255

18 March 2024 Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Yuanxue Ding, Yanchen Qu, Di Liu, Xin Zhou, Pengfei Wang, Ruiqi Zhou, Zhihui Liu, Yuebing Zhu

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131040N (2024) https://doi.org/10.1117/12.3021255
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

In the military and civil fields, detecting small aircraft is of great significance. In recent years, the rapid development of LiDAR technology has made it possible to detect small aircraft at long distances. However, the scale change and attitude change make the detection difficult. Therefore, a detection network of multi-attitude small aircraft based on LiDAR anchorfree is proposed in this paper. The network structure is improved on the basis of the CenterNet network; using the encoder-decoder network structure, the extended convolutional module is designed to improve the receptive field and obtain the multi-scale information of the object. The IOU sensing branch is added to the detection header of the network to improve the localization accuracy of the object. The experimental results show that the detection accuracy of the improved network on the self-built simulation data set is 2.12% higher than that before the improvement and finally reaches 92.45%. Therefore, using this method can effectively improve the detection accuracy of the object.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanxue Ding, Yanchen Qu, Di Liu, Xin Zhou, Pengfei Wang, Ruiqi Zhou, Zhihui Liu, and Yuebing Zhu "Multi-attitude small aircraft object detection based on Gm-APD LiDAR", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131040N (18 March 2024); https://doi.org/10.1117/12.3021255

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KEYWORDS

Object detection

LIDAR

Deep learning

Detection and tracking algorithms

Education and training

Head

Remote sensing

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