Image Object Detection Algorithm for Autonomous Vehicles

DOI

10.2991/978-94-6463-512-6_26How to use a DOI?

Keywords

Machine Learning; Autonomous Vehicle; Object Detection

Abstract

To improve the visual perception skills necessary for safe and effective operation, this thesis investigates the use of image object detection algorithms in autonomous vehicle systems. One essential part of the sensory framework of autonomous vehicles is object detection, which is the process of recognizing and locating different objects in the area around the vehicle. Three well-known algorithms—: Region-based Convolutional Neural Networks, You Only Look Once and Single Shot MultiBox Detector—that are each recognized for their distinct methods of processing and interpreting visual data are the main focus of this study’s evaluation. An overview of the history of autonomous driving technologies is given at the outset of the study, with a focus on the importance of object detection for visual perception systems. The thesis compares the benefits and drawbacks of R-CNN, YOLO and SSD, focusing on detection accuracy, processing speed and adaptability to environmental changes. The performance of these algorithms in various driving scenarios is highlighted by the experimental results, which provide a solid assessment of their usefulness in autonomous driving. Aim to further enhance autonomous vehicle technologies by improving object detection capabilities, the conclusion reviews the research findings and makes recommendations for future developments and research directions.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fangzhou Liu
PY  - 2024
DA  - 2024/09/23
TI  - Image Object Detection Algorithm for Autonomous Vehicles
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 225
EP  - 233
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_26
DO  - 10.2991/978-94-6463-512-6_26
ID  - Liu2024
ER  -