Robust 3D Object Detection for Moving Objects Based on PointPillars

Nakamura, Ryota; Enokida, Shuichi

doi:10.1109/wacvw54805.2022.00067

Cited by 2 publications

(1 citation statement)

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“…Several scholars from various countries have proposed different methods to address this issue. Ryota et al suggest varying voxel sizes and carrying out feature fusion after extracting the features [7]. Li et al recommend incorporating the attention mechanism within the voxel and introducing relationship features between diverse point clouds [8].…”

Section: Introductionmentioning

confidence: 99%

Research on PointPillars Algorithm Based on Feature-Enhanced Backbone Network

Shu,

Zhang

2024

Electronics

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In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accuracy in the PointPillars 3D target detection algorithm, this paper proposes an algorithm based on feature enhancement to improve the backbone network. The algorithm enhances preliminary feature information of the backbone network by modifying it based on PointPillars with the aid of channel attention and spatial attention mechanisms. To address the inefficiency caused by the excessive number of subsampled parameters in PointPillars, FasterNet (a lightweight and efficient feature extraction network) is utilized for down-sampling and forming different scale feature maps. To prevent the loss and blurring of extracted features resulting from the use of inverse convolution, we utilize the lightweight and efficient up-sampling modules Carafe and Dysample for adjusting resolution. Experimental results indicate improved accuracy under all difficulties of the KITTI dataset, demonstrating the superiority of the algorithm over PointPillars.

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Section: Introductionmentioning

confidence: 99%