A Deep Learning-Based Approach for Road Surface Damage Detection

Kulambayev B. Beissenova G. Katayev N. Abduraimova B. Zhaidakbayeva L. Sarbassova A. Akhmetova O. Issayev S. Suleimenova L. Kasenov S. Shadinova K. Shyrakbayev A.
2022 Tech Science Press

Computers, Materials and Continua
2022 #73 Issue 2 3403 - 3418 pp.

Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road accidents. Currently, various methods of photo and video surveillance are used to monitor the condition of the road surface. The manual approach to evaluation and analysis of the received data can take a protracted period of time. Thus, it is necessary to improve the procedures for inspection and assessment of the condition of control objects with the help of computer vision and deep learning techniques. In this paper, we propose a model based on Mask Region-based Convolutional Neural Network (Mask R-CNN) architecture for identifying defects of the road surface in the real-time mode. It shows the process of collecting and the features of the training samples and the deep neural network (DNN) training process, taking into account the specifics of the problems posed. For the software implementation of the proposed architecture, the Python programming language and the TensorFlow framework were utilized. The use of the proposed model is effective even in conditions of a limited amount of source data. Also as a result of experiments, a high degree of repeatability of the results was noted. According to the metrics, Mask R-CNN gave the high detection and segmentation results showing 0.9214, 0.9876, 0.9571 precision, recall, and F1-score respectively in road damage detection, and Intersection over Union (IoU)-0.3488 and Dice similarity coefficient-0.7381 in segmentation of road damages.

deep learning , detection , mask R-CNN , Road damage , segmentation

Text of the article Перейти на текст статьи

International Information Technology University, Almaty, Kazakhstan
M.Auezov South Kazakhstan University, Shymkent, Kazakhstan
University of Friendship of People’s Academician A. Kuatbekov, Shymkent, Kazakhstan
Kazakh National Women’s Teacher Training University, Almaty, Kazakhstan
L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
South Kazakhstan State Pedagogical University, Shymkent, Kazakhstan
Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
International Taraz Innovative Institute, Taraz, Kazakhstan

International Information Technology University
M.Auezov South Kazakhstan University
University of Friendship of People’s Academician A. Kuatbekov
Kazakh National Women’s Teacher Training University
L.N. Gumilyov Eurasian National University
Al-Farabi Kazakh National University
Abai Kazakh National Pedagogical University
South Kazakhstan State Pedagogical University
Asfendiyarov Kazakh National Medical University
International Taraz Innovative Institute

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026