Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors
Open accessAbstract
The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate prediction of objects in the vicinity to guarantee safer journeys. For effectively predicting objects, sensors such as Three-Dimensional Light Detection and Ranging (3D LiDAR) and cameras can be used. The 3D LiDAR sensor captures the 3D shape of the object and produces point cloud data that describes the geometrical structure of the object. The LiDAR-only detectors may be subject to false detection or even non-detection over objects located at high distances. The camera sensor captures RGB images with sufficient attributes that describe the distinct identification of the object. The high-resolution images produced by the camera sensor benefit the precise classification of the objects. However, hindrances such as the absence of depth information from the images, unstructured point clouds, and cross modalities affect assertion and boil down the environmental perception. To this end, this paper proposes an object detection mechanism that fuses the data received from the camera sensor and the 3D LiDAR sensor (OD-C3DL). The 3D LiDAR sensor obtains point clouds of the object such as distance, position, and geometric shape. The OD-C3DL employs Convolutional Neural Networks (CNN) for further processing point clouds obtained from the 3D LiDAR sensor and the camera sensor to recognize the objects effectively. The point cloud of the LiDAR is enhanced and fused with the image space on the Regions of Interest (ROI) for easy recognition of the objects. The evaluation results show that the OD-C3DL can provide an average of 89 real-time objects for a frame and reduces the extraction time by a recall rate of 94%. The average processing time is 65ms, which makes the OD-C3DL model incredibly suitable for the AVs perception. Furthermore, OD-C3DL provides mean accuracy for identifying automobiles and pedestrians at a moderate degree of difficulty is higher than that of the previous models at 79.13% and 88.76%.
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Shape-based object detection uses computer vision algorithms to analyze the shapes and contours of objects in video footage, enabling detection and classification of specific objects of interest.
“Shape-based object detection in video surveillance uses computer vision algorithms to analyze the shapes and contours of objects that are captured in video footage, which enables the detection and classification of specific objects of interest.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [77]
The Introduction and Section 3.3 describe camera images, CNN-based feature extraction, and object detection and classification. But the paper concerns autonomous driving and combines camera data with LiDAR data for geometric shape information; it does not describe analyzing shapes and contours in video-surveillance footage. Page numbers are not available in our copy. (LLM, conf=0.86)
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
- Authors
- Arikumar, K. S., Deepak Kumar, A., Gadekallu, T. R., Prathiba, S. B., & Tamilarasi, K.
- Affiliations
- School of Computer Science and Engineering, VIT-AP University, Vijayawada 522237, India
- Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai 600119, India
- Lebanese American University (LB)
- Vellore Institute of Technology University (IN)
- Corresponding author
- Prathiba, Sahaya Beni
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-12-16
- Volume
- 11
- Issue
- 24
- Pages
- 4203
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4313293021
- Cited by
- 46
- References
- 44
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 14 · 2023: 15
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- 3D object detection, autonomous vehicles, 3D LiDAR, point clouds, real-time object detection, cross-modal fusion, region of interest, environmental perception, convolutional neural networks
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