Improved pointpillar point cloud object detection based on channel attention mechanism

Gao Tu; Yingnan Geng

doi:10.1117/12.3050685

24 October 2024 Improved pointpillar point cloud object detection based on channel attention mechanism

Gao Tu, Yingnan Geng

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960N (2024) https://doi.org/10.1117/12.3050685
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

To upgrade the exactness of the 3D point cloud target location calculation, this investigate presents an progressed PointPillars strategy leveraging spatial consideration and channel components. At first, we coordinated a refined point cloud include representation into the column include arrange, upgrading the coding of highlights and making strides the uniqueness of each point's representation. Secondly, we join a spatial consideration component into the pseudo-image handling. This method recalibrates the highlight weights of spatially encoded focuses, subsequently boosting the algorithm's capability to extricate basic highlights and upgrading location performance. Experimental approval on the broadly utilized KITTI dataset illustrates critical enhancements in discovery exactness and soundness compared to the initial calculation.

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

Citation Download Citation

Gao Tu and Yingnan Geng "Improved pointpillar point cloud object detection based on channel attention mechanism", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960N (24 October 2024); https://doi.org/10.1117/12.3050685

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KEYWORDS

Point clouds

Object detection

Convolution

Feature extraction

Detection and tracking algorithms

LIDAR

3D acquisition

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