Advancements in road surface classification: a comprehensive review of computer vision and IoT approaches
The rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by...
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| Published in: | International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23) Vol. 2023; pp. 31 - 36 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
The Institution of Engineering and Technology
2023
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| Online Access: | Get full text |
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| Summary: | The rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by investigating diverse methodologies within the realms of computer vision and the Internet of Things (IoT). Through an extensive investigation, various techniques encompassing Image processing, Machine Learning(ML), Deep Learning(DL), and IoT are examined for their effectiveness in classifying road surfaces at different terrains. Moreover, this article also reviews datasets, signals, sensors, communication protocols, IoT implementation strategies, pre-processing methodologies, and feature extraction techniques. This investigation delves into novel approaches aimed at resolving road surface classification challenges, meticulously examining their respective strengths and limitations. Furthermore, this article includes a comparative analysis of these advanced methodologies, facilitating the identification of the most suitable model for the task. The assessment takes into account intricate methodological aspects, types of sensors/cameras, dataset variations, and performance metrics, thereby providing valuable insights into the landscape of road surface classification for autonomous driving applications. |
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| DOI: | 10.1049/icp.2023.2849 |