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3D Lidar Target Detection Method at the Edge for the Cloud Continuum

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Abstract

In the internet of things, machine learning at the edge of cloud continuum is developing rapidly, providing more convenient services for design developers. The paper proposes a lidar target detection method based on scene density-awareness network for cloud continuum. The density-awareness network architecture is designed, and the context column feature network is proposed. The BEV density attention feature network is designed by cascading the density feature map with the spatial attention mechanism, and then connected with the BEV column feature network to generate the ablation BEV map. Multi-head detector is designed to regress the object center point, scale size and direction, and loss function is used for active supervision. The experiment is conducted on Alibaba Cloud services. On the validation dataset of KITTI, the 3D objects and BEV objects are detected and evaluated for three types of objects. The results show that most of the AP values of the density-awareness model proposed in this paper are higher than other methods, and the detection time is 0.09 s, which can meet the requirements of high accuracy and real-time of vehicle-borne lidar target detection.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors acknowledge the Natural Science Foundation of Jilin Province of China (Grant: YDZJ202201ZYTS655).

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Authors and Affiliations

  1. School of Mechanical and Control Engineering, Baicheng Normal University, Baicheng, 137000, China

    Xuemei Li & Chong Chen

  2. Xi’an Key Laboratory of Active Photoelectric Imaging Detection Technology, Xi’an Technological University, Xi’an, 710000, China

    Xuelian Liu

  3. School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China

    Da Xie

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  1. Xuemei Li
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  2. Xuelian Liu
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  3. Da Xie
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  4. Chong Chen
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Contributions

Xuemei Li carried out the design of methods and the writing of the manuscript. Xuelian Liu carried out the design of methods and the guidance of the manuscript writing. Da Xie carried out the software design and model validation. Chong Chen carried out the model training and data management. All authors read and approved the final manuscript.

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Correspondence to Xuelian Liu.

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Li, X., Liu, X., Xie, D. et al. 3D Lidar Target Detection Method at the Edge for the Cloud Continuum. J Grid Computing 22, 14 (2024). https://doi.org/10.1007/s10723-023-09736-0

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