LiDAR Point Clouds Semantic Segmentation in Autonomous Driving Based on Asymmetrical Convolution
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Published:2023-12-07
Issue:24
Volume:12
Page:4926
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Sun Xiang1ORCID, Song Shaojing2ORCID, Miao Zhiqing3, Tang Pan4, Ai Luxia5ORCID
Affiliation:
1. School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China 2. School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China 3. School of Communication and Electronic Engineering, East China Normal University, Shanghai 200062, China 4. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China 5. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract
LiDAR has become a vital sensor for autonomous driving scene understanding. To meet the accuracy and speed of LiDAR point clouds semantic segmentation, an efficient model ACPNet is proposed in this paper. In the feature extraction stage, the backbone is constructed with asymmetric convolutions, so the skeleton of the square convolution kernel is enhanced, which leads to greater robustness to target rotation. Moreover, a contextual feature enhancement module is designed to extract richer contextual features. During training, global scaling and global translation are performed to enrich the diversity of datasets. Compared with the baseline network PolarNet, the mIoU of ACPNet on the SemanticKITTI, SemanticPOSS and nuScenes datasets are improved by 5.1%, 1.6% and 2.9%, respectively. Meanwhile, the speed of ACPNet is 14 FPS, which basically meets the real-time requirements in autonomous driving scenarios. The experimental results show that ACPNet significantly improves the performance of LiDAR point cloud semantic segmentation.
Funder
Discipline Construction of Computer Science and Technology of Shanghai Polytechnic University
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference42 articles.
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