Precise Analysis of Road Fissures Detection under Ccomplex Road Conditions based on Deep Learning

DOI

10.2991/978-94-6463-300-9_6How to use a DOI?

Keywords

Road fissures detection; complex scenes; deep learning

Abstract

Road crack recognition and detection is one of the fundamental tasks in the fields of autonomous driving and intelligent transportation, which has attracted a lot of research interest in recent years. Thanks to the rapid development of Convolutional neural network, the accuracy of road crack recognition based on depth learning is continuously improved, while few of these methods focus on the complex road scenes. This research undertook a thorough accuracy analysis of the utilization of Convolutional Neural Networks (CNNs) in recognizing road cracks under complex road conditions. It meticulously examined the performance of CNNs, a sophisticated form of deep learning model, in identifying and differentiating road surface cracks in challenging circumstances, such as water-logged road surfaces, pedestrian interference, and the presence of shadows. The study scrutinized the capacity of CNNs to automatically extract and learn salient features from images, a pivotal aspect in the precise detection of road surface cracks. Moreover, the adaptability of CNNs to diverse and complex environments, their ability to comprehend intricate patterns essential for accurate crack recognition, and their robustness against fluctuating environmental conditions were put under rigorous evaluation. The research hence embodied an exhaustive exploration into the efficacy of CNNs in road crack detection under complex road conditions, illuminating both their potential strengths and areas requiring further enhancement.

Copyright

© 2023 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  - Hui Li
PY  - 2023
DA  - 2023/11/27
TI  - Precise Analysis of Road Fissures Detection under Ccomplex Road Conditions based on Deep Learning
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 43
EP  - 56
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_6
DO  - 10.2991/978-94-6463-300-9_6
ID  - Li2023
ER  -