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

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

Received:
October 24, 2023
Accepted:
February 1, 2024
Published:
May 20, 2024
Keywords:
road surface marking, uncertainty aware, CNN
Abstract

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.
Data files:
References
  1. [1] D. Babić, D. Babić, M. Fiolic, and M. Ferko, “Road Markings and Signs in Road Safety,” Encyclopedia, Vol.2, No.4, pp. 1738-1752, 2022. https://doi.org/10.3390/encyclopedia2040119
  2. [2] A. El Krine, M. Redondin, J. Girard, C. Heinkele, A. Stresser, and V. Muzet, “Does the Condition of the Road Markings Have a Direct Impact on the Performance of Machine Vision During the Day on Dry Roads?,” Vehicles, Vol.5, No.1, Article No.286305, 2023. https://doi.org/10.3390/vehicles5010016
  3. [3] A. M. Pike, T. P. Barrette, and P. J. Carlson, “Evaluation of the Effects of Pavement Marking Characteristics on Detectability by ADAS Machine Vision,” National Cooperative Highway Research Program Transportation Research Board of The National Academies of Sciences, Engineering, and Medicine, Project No.20-102 (06), 2018.
  4. [4] S. Xu, J. Wang, P. Wu, W. Shou, X. Wang, and M. Chen, “Vision-Based Pavement Marking Detection and Condition Assessment–A Case Study,” Applied Sciences, Vol.11, No.11, Article No.3152, 2021. https://doi.org/10.3390/app11073152
  5. [5] S. Lee and B. H. Cho, “Evaluating Pavement Lane Markings in Metropolitan Road Networks with a Vehicle-Mounted Retroreflectometer and AI-Based Image Processing Techniques,” Remote Sensing, Vol.15, No.7, Article No.1812, 2023. https://doi.org/10.3390/rs15071812
  6. [6] C. Dewi, R.-C. Chen, Y.-C. Zhuang, X. Jiang, and H. Yu, “Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips,” Big Data and Cognitive Computing, Vol.7, No.1, Article No.54, 2023. https://doi.org/10.3390/bdcc7010054
  7. [7] Y. Tang, Z. Ni, J. Zhou, D. Zhang, J. Lu, Y. Wu, and J. Zhou, “UA Score Distribution Learning for Action Quality Assessment,” arXiv:2006.07665, 2020. https://doi.org/10.48550/arXiv.2006.07665
  8. [8] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556v6, 2015. https://doi.org/10.48550/arXiv.1409.1556
  9. [9] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” arXiv:1611.05431, 2016. https://arxiv.org/abs/1611.05431
  10. [10] R. Ito, H. Nobuhara, and S. Kato, “Transfer Learning Method for Object Detection Model Using Genetic Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 776-783, 2022. https://doi.org/10.20965/jaciii.2022.p0776
  11. [11] Y. Yasuoka, Y. Shinomiya, and Y. Hoshino, “Simulation of Human Detection System Using BRIEF and Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1159-1164, 2016. https://doi.org/10.20965/jaciii.2016.p1159
  12. [12] D. Long, “A Facial Expressions Recognition Method Using Residual Network Architecture for Online Learning Evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.6, pp. 953-962, 2021. https://doi.org/10.20965/jaciii.2021.p0953
  13. [13] K. Han, W. Wan, H. Yao, and L. Hou, “Image Crowd Counting Using Convolutional Neural Network and Markov Random Field,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.4, pp. 632-638, 2017. https://doi.org/10.20965/jaciii.2017.p0632
  14. [14] Z. Du, Y. Jin, H. Ma, and P. Liu, “A Lightweight and Accurate Method for Detecting Traffic Flow in Real Time,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.6, pp. 1086-1095, 2023. https://doi.org/10.20965/jaciii.2023.p1086
  15. [15] S. Ohkawa, H. Shinya, and Y. Takita, “1308 Detection Method of Road Marking Using LRF Intensity of Surface,” The Proc. of the Transportation and Logistics Conf., Vol.2013.22, pp. 249-252, 2013 (in Japanese). https://doi.org/10.1299/jsmetld.2013.22.249
  16. [16] Z. Liu, S. Yu, X. Wang, and N. Zheng, “Detecting Drivable Area for Self-Driving Cars: An Unsupervised Approach,” arXiv:1705.00451v1, 2017. https://doi.org/10.48550/arXiv.1705.00451
  17. [17] B. Li, D. Song, H. Li, A. Pike, and P. Carlson, “Lane Marking Quality Assessment for Autonomous Driving,” 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2018. https://doi.org/10.1109/IROS.2018.8593855
  18. [18] M. V. Medvedev and V. I. Pavlov, “Road Surface Marking Recognition and Road Surface Quality Evaluation Using Convolution Neural Network,” 2020 Int. Multi-Conf. on Industrial Engineering and Modern Technologies (FarEastCon), 2020. https://doi.org/10.1109/FarEastCon50210.2020.9271368
  19. [19] T. Lee, Y. Yoon, C. Chun, and S. Ryu, “CNN-Based Road-Surface Crack Detection Model that Responds to Brightness Changes,” Electronics, Vol.10, No.12, Article No.1402, 2021. https://doi.org/10.3390/electronics10121402
  20. [20] K.-L. Lin, T.-C. Wu, and Y.-R. Wang, “An Innovative Road Marking Quality Assessment Mechanism Using Computer Vision,” Advances in Mechanical Engineering, Vol.8, No.6, 2016. https://doi.org/10.1177/1687814016654043
  21. [21] A. R. Stacy, “Evaluation of Machine Vision Collected Pavement Marking Quality Data for Use in Transportation Asset Management,” Master’s thesis, Texas A&M University, 2019.
  22. [22] F. Asdrubali, C. Buratti, E. Moretti, F. D’Alessandro, and S. Schiavoni, “Assessment of the Performance of Road Markings in Urban Areas: The Outcomes of the Civitas Renaissance Project,” The Open Transportation J., Vol.7, pp. 7-19, 2013. http://dx.doi.org/10.2174/1874447801307010007
  23. [23] R. Mukherjee, H. Iqbal, S. Marzban, A. Badar, T. Brouns, S. Gowda, E. Arani, and B. Zonooz, “AI Driven Road Maintenance Inspection,” 27th ITS World Congress, 2021.
  24. [24] S. Ji, W. Xu, M. Yang, and K. Yu, “3D Convolutional Neural Networks for Human Action Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.35, No.1, pp. 221-231, 2013. https://doi.org/10.1109/TPAMI.2012.59
  25. [25] M. Boudissa, H. Kawanaka, and T. Wakabayashi, “Semantic Segmentation of Traffic Landmarks Using Classical Computer Vision and U-Net Model,” Int. Conf. of Engineering Technology (ICET), 2021.
  26. [26] M. Boudissa, H. Kawanaka, and T. Wakabayashi, “Traffic Landmark Quality Evaluation Using Efficient VGG-16 Model,” 2022 Joint 12th Int. Conf. on Soft Computing and Intelligent Systems and 23rd Int. Symp. on Advanced Intelligent Systems (SCIS-ISIS), 2020. https://doi.org/10.1109/SCISISIS55246.2022.10002145
  27. [27] M. Joyce, “Kullback–Leibler Divergence,” M. Lovric (Ed.), “International Encyclopedia of Statistical Science,” pp. 720-722, Springer, 2011. https://doi.org/10.1007/978-3-642-04898-2
  28. [28] Q. Lei, J. Liu, M. Wu, and J. Wang, “Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1035-1043, 2016. https://doi.org/10.20965/jaciii.2016.p1035
  29. [29] D. Katsuma, H. Kawanaka, V. Prasath, and B. Aronow, “Data Augmentation Using Generative Adversarial Networks for Multi-Class Segmentation of Lung Confocal IF Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.2, pp. 138-146, 2022. https://doi.org/10.20965/jaciii.2022.p0138

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