Article
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Research on PointPillars Algorithm Based on Feature-Enhanced Backbone Network
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Version 1
: Received: 11 January 2024 / Approved: 12 January 2024 / Online: 12 January 2024 (09:12:32 CET)
A peer-reviewed article of this Preprint also exists.
Shu, X.; Zhang, L. Research on PointPillars Algorithm Based on Feature-Enhanced Backbone Network. Electronics 2024, 13, 1233. Shu, X.; Zhang, L. Research on PointPillars Algorithm Based on Feature-Enhanced Backbone Network. Electronics 2024, 13, 1233.
Abstract
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 downsampling 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.
Keywords
Target Detection; PointPillars; FasterNet; Upsampling; Attention Mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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