Academic Journal
3D object detection for vehicle-mounted LiDAR based on deep learning and euclidean clustering algorithm.
| Τίτλος: | 3D object detection for vehicle-mounted LiDAR based on deep learning and euclidean clustering algorithm. |
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| Συγγραφείς: | Zhang N; School of Control Engineering, Wuxi University of Technology, Wuxi, China., Xi M; School of Control Engineering, Wuxi University of Technology, Wuxi, China., Fang J; School of Control Engineering, Wuxi University of Technology, Wuxi, China., Wang F; School of Control Engineering, Wuxi University of Technology, Wuxi, China. |
| Πηγή: | PloS one [PLoS One] 2026 Jun 01; Vol. 21 (6), pp. e0348581. Date of Electronic Publication: 2026 Jun 01 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Imaging, Three-Dimensional*/methods , Deep Learning* , Detection Algorithms* , Autonomous Vehicles*, Cluster Analysis ; Clustering Algorithms |
| Περίληψη: | Object Detection (OD) stands as a fundamental task in the area of autonomous driving environment perception. This study introduces a 3D OD method grounded in deep learning and an improved Euclidean clustering algorithm, aiming to improve the accuracy and efficiency of point cloud segmentation and OD. The core methodological innovations include: (1) the integration of the Cloth Simulation Filter (CSF) for accurate ground and non-ground point separation, combined with a K-Dimensional Tree (KD-Tree) structure and an adaptive parameter mechanism to enhance clustering robustness and efficiency; and (2) an enhanced PointNet architecture incorporating multi-scale grouping (MSG), multi-resolution grouping (MRG), and skip connections to improve local feature extraction and multi-level feature fusion. This method is differentiated from prior works by its holistic integration of density-aware segmentation and hierarchical feature aggregation, addressing key bottlenecks in handling sparse and uneven LiDAR data. The proposed method is rigorously evaluated on the KITTI and NuScenes benchmarks. It achieves segmentation accuracies of 94.96% and 93.12%, with single-frame processing times of 15.63 ms and 17.24 ms, respectively, demonstrating a superior balance of speed and precision compared to traditional Euclidean clustering and other baseline methods. For the 3D OD task, the model attains average detection accuracies of 94.36% and 92.68% on the respective datasets, representing statistically significant improvements (p < 0.001) over the standard PointNet. The detection speed reaches 34 fps and 31 fps, meeting real-time requirements while outperforming existing frameworks in challenging scenarios involving occluded and multi-scale objects. The findings confirm that the proposed framework provides a robust, efficient, and generalizable solution for 3D environmental perception in autonomous driving systems. (Copyright: © 2026 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| References: | IEEE Trans Pattern Anal Mach Intell. 2023 Jul;45(7):9055-9071. (PMID: 36455091) |
| Entry Date(s): | Date Created: 20260601 Date Completed: 20260601 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13225636 |
| DOI: | 10.1371/journal.pone.0348581 |
| PMID: | 42224307 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0348581 |