Distance measurement and leakage removal based on autonomous perception fusion

Yiding Hua; Zixi Li; Hui Rong; Hanbing Wu; Hang Sun

doi:10.1117/12.3011528

7 December 2023 Distance measurement and leakage removal based on autonomous perception fusion

Yiding Hua, Zixi Li, Hui Rong, Hanbing Wu, Hang Sun

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412W (2023) https://doi.org/10.1117/12.3011528
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Target detection in 3D vision holds an important role in the visual understanding system of self-driving cars, and the use of multiple sensors can effectively overcome the limitations of a single sensor and enhance the accuracy and reliability of the intelligent vehicle's perception of the surrounding environment. So, the research generally adopts the method of fusion of multisensor information to detect the target. In this paper, the distance measurement of the target is accomplished through data fusion between the LiDAR point cloud data and images. Furthermore, the proposed distance measurement technology based on R-tree is based on target recognition with images captured by the on-board camera. A leakage detection removal technique based on radar point cloud enhancement is proposed to solve the problem of serious interference with the recognition of deep neural networks caused by the poor imaging quality of photos taken by vehicle-mounted cameras in special scenes and conditions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiding Hua, Zixi Li, Hui Rong, Hanbing Wu, and Hang Sun "Distance measurement and leakage removal based on autonomous perception fusion", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412W (7 December 2023); https://doi.org/10.1117/12.3011528

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KEYWORDS

Object detection

Point clouds

Radar sensor technology

LIDAR

Target detection

Distance measurement

Neural networks

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