C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles
Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based <b>2D object detection using YOLOv8</b> and LiDAR data-based <b>3D object detection us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2688 |
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| author | Thanh Binh Ngo Long Ngo Anh Vu Phi Trung Thị Hoa Trang Nguyen Andy Nguyen Jason Brown Asanka Perera |
| author_facet | Thanh Binh Ngo Long Ngo Anh Vu Phi Trung Thị Hoa Trang Nguyen Andy Nguyen Jason Brown Asanka Perera |
| author_sort | Thanh Binh Ngo |
| collection | DOAJ |
| description | Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based <b>2D object detection using YOLOv8</b> and LiDAR data-based <b>3D object detection using PointPillars, hence named C2L3-Fusion</b>. Unlike conventional fusion approaches, which often struggle with feature misalignment, <b>C2L3-Fusion</b> enhances spatial consistency and multi-level feature aggregation, significantly improving detection accuracy. Our method outperforms state-of-the-art approaches such as YoPi-CLOCs Fusion Network, standalone YOLOv8, and standalone PointPillars, achieving mean Average Precision (mAP) scores of <b>89.91% (easy), 79.26% (moderate), and 78.01% (hard)</b> on the KITTI dataset. Successfully implemented on the Nvidia Jetson AGX Xavier embedded platform, <b>C2L3-Fusion</b> maintains real-time performance while enhancing robustness, making it highly suitable for self-driving vehicles. This paper details the methodology, mathematical formulations, algorithmic advancements, and real-world testing of C2L3-Fusion, offering a comprehensive solution for 3D object detection in autonomous navigation. |
| format | Article |
| id | doaj-art-99ac59cbebbb456a9ce66a892ffec4f6 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-99ac59cbebbb456a9ce66a892ffec4f62025-08-20T01:49:11ZengMDPI AGSensors1424-82202025-04-01259268810.3390/s25092688C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous VehiclesThanh Binh Ngo0Long Ngo1Anh Vu Phi2Trung Thị Hoa Trang Nguyen3Andy Nguyen4Jason Brown5Asanka Perera6Department of Electrical and Electronic Engineering, University of Transport and Communications, Hanoi 100000, VietnamSoftware and Service Development Department, Mobifone Digital Services, Hanoi 100000, VietnamComputer Science Department, College of Engineering, Michigan State University, East Lansing, MI 48823, USADepartment of Electrical and Electronic Engineering, University of Transport and Communications, Hanoi 100000, VietnamSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, AustraliaSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, AustraliaSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, AustraliaAccurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based <b>2D object detection using YOLOv8</b> and LiDAR data-based <b>3D object detection using PointPillars, hence named C2L3-Fusion</b>. Unlike conventional fusion approaches, which often struggle with feature misalignment, <b>C2L3-Fusion</b> enhances spatial consistency and multi-level feature aggregation, significantly improving detection accuracy. Our method outperforms state-of-the-art approaches such as YoPi-CLOCs Fusion Network, standalone YOLOv8, and standalone PointPillars, achieving mean Average Precision (mAP) scores of <b>89.91% (easy), 79.26% (moderate), and 78.01% (hard)</b> on the KITTI dataset. Successfully implemented on the Nvidia Jetson AGX Xavier embedded platform, <b>C2L3-Fusion</b> maintains real-time performance while enhancing robustness, making it highly suitable for self-driving vehicles. This paper details the methodology, mathematical formulations, algorithmic advancements, and real-world testing of C2L3-Fusion, offering a comprehensive solution for 3D object detection in autonomous navigation.https://www.mdpi.com/1424-8220/25/9/2688AIdeep learningC2L3-Fusion2D detection3D detectionautonomous vehicle |
| spellingShingle | Thanh Binh Ngo Long Ngo Anh Vu Phi Trung Thị Hoa Trang Nguyen Andy Nguyen Jason Brown Asanka Perera C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles Sensors AI deep learning C2L3-Fusion 2D detection 3D detection autonomous vehicle |
| title | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
| title_full | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
| title_fullStr | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
| title_full_unstemmed | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
| title_short | C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles |
| title_sort | c2l3 fusion an integrated 3d object detection method for autonomous vehicles |
| topic | AI deep learning C2L3-Fusion 2D detection 3D detection autonomous vehicle |
| url | https://www.mdpi.com/1424-8220/25/9/2688 |
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