[1]LU Jun,LI Yang,LU Linchao.Long-distance and occluded 3D target detection algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(2):259-266.[doi:10.11992/tis.202301001]
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Long-distance and occluded 3D target detection algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 259-266 Column: 学术论文—机器学习 Public date: 2024-03-05
Title:
Long-distance and occluded 3D target detection algorithm
Author(s):
LU Jun;  LI Yang;  LU Linchao
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
target detection; deep learning; Lidar point cloud; long-distance target; occluded target; autopilot; regional pyramid; feature extraction
CLC:
TP391.41
DOI:
10.11992/tis.202301001
Abstract:
To address the limitations of existing 3D target detection algorithms, particularly their poor detection performance for occluded and long-distance objects, we have implemented an enhancement to the PointRCNN network, a 3D object detection algorithm based on point cloud. We began by voxelizing the region of interest obtained from the region proposal network and constructing region pyramids of different scales to capture a wider range of points of interest. Simultaneously, we introduced a point cloud transformer module to enhance the learning of the local features of grid center points. Moreover, we incorporated a sphere query radius prediction module into the network. This addition allows the model to adaptively adjust the sphere query range according to the density of the point cloud. Finally, the effectiveness of the proposed algorithm was validated through rigorous experimental testing. We evaluated the performance of the model using the KITTI data set and designed corresponding ablation experiments to verify the effectiveness of each module in the model.
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