A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles

Ahmed, Imran ORCID logoORCID: https://orcid.org/0000-0002-7751-286X, Jeon, Gwanggil and Chehri, Abdellah (2022) A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. ISSN 1558-0016

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Official URL: https://doi.org/10.1109/TITS.2022.3210490


Integration of advanced signal processing, image processing, deep learning, edge computing, and the Internet of Things (IoT) into vehicles allows intelligent automated vehicles to navigate autonomously in different environments. It is crucial for reliable and safe driving that an autonomous vehicle can accurately, effectively, and efficiently recognize, perceive, and observe the surrounding environments. Autonomous vehicles comprise advanced sensor technologies such as RGB cameras and LiDaR that produce an extensive data set in the form of RGB images and 3D measurement points, also recognized as a point cloud. It is necessary to understand and interpret collected data information efficiently and to identify other road users, such as pedestrians and vehicles. Thus, we introduced a smart IoT-enabled deep learning based end-to-end 3D object detection system that works in real-time, emphasizing autonomous driving situations. The detection model is based on YOLOv3; firstly, the model is utilized for 2D object detection and then modified for 3D object detection purposes. The presented model uses point cloud, and RGB image data as input and outputs detected bounding boxes with confidence scores and class labels. Experiments are carried out on the Lyft data set; results reveal that the YOLOv3 model achieves high accuracy and outperforms from other state-of-the-art detection models in terms of effectiveness and accuracy. The overall accuracy of the model is 96% and 97% for 2D and 3D object detection, respectively.

Item Type: Journal Article
Keywords: Object detection, Three-dimensional displays, Autonomous vehicles, Laser radar, Cameras
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 23 Nov 2022 14:16
Last Modified: 23 Nov 2022 14:16
URI: https://arro.anglia.ac.uk/id/eprint/708006

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