JACIII Vol.28 No.3 pp. 634-643
doi: 10.20965/jaciii.2024.p0634

Research Paper:

Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining

Mehieddine Boudissa, Hiroharu Kawanaka ORCID Icon, and Tetsushi Wakabayashi

Division of Systems Engineering, Graduate School of Engineering, Mie University
1577 Kurimamachiya-cho, Tsu-shi, Mie 514-8507, Japan

Corresponding author

October 24, 2023
February 1, 2024
May 20, 2024
road surface marking, uncertainty aware, CNN

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called “progressive pretraining (PPT).” We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular.

Road surface marking quality evaluation

Road surface marking quality evaluation

Cite this article as:
M. Boudissa, H. Kawanaka, and T. Wakabayashi, “Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 634-643, 2024.
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Last updated on Jul. 12, 2024