Year: 2025
Type: article
Abstract: To address the limitation of receptive fields caused by the use of local convolutions in current point cloud object detection methods, this paper proposes a LiDAR point cloud object detection algorithm that integrates global features. The proposed method employs a Voxel Mapping Block (VMB) and a Global Feature Extraction Block (GFEB) to convert the point cloud data into a one-dimensional long sequence. It then utilizes non-local convolutions to model the entire voxelized point cloud and incorporate global contextual information, thereby enhancing the network’s receptive field and its capability to extract and learn global features. Furthermore, a Voxel Channel Feature Extraction (VCFE) module is designed to capture local spatial information by associating features across different channels, effectively mitigating the spatial information loss introduced during the one-dimensional transformation. The experimental results demonstrate that, compared with state-of-the-art methods, the proposed approach improves the average precision of vehicle, pedestrian, and cyclist targets on the Waymo subset by 0.64%, 0.71%, and 0.66%, respectively. On the nuScenes dataset, the detection accuracy for var targets increased by 0.7%, with NDS and mAP improving by 0.3% and 0.5%, respectively. In particular, the method exhibits outstanding performance in small object detection, significantly enhancing the overall accuracy of point cloud object detection.
Source: Information
DOI: 10.3390/info16100832
Authors: Xu Zhang, Fengchang Tian, Jiaxing Sun, Yan Liu
Institution: Zhengzhou University of Light Industry
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4414556809