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A Deep Learning-Based Approach for Road Surface Damage Detection

Computers, Materials & Continua 2022年第11期73卷 3403-3418页

作者： Bakhytzhan Kulambayev Gulbakhram Beissenova Nazbek Katayev Bayan Abduraimova Lyazzat Zhaidakbayeva Alua Sarbassova Oxana Akhmetova Sapar Issayev Laura Suleimenova Syrym Kasenov kunsulu shadinova Abay Shyrakbayev International Information Technology University AlmatyKazakhstan M.Auezov South Kazakhstan University ShymkentKazakhstan University of Friendship of People’s Academician A.Kuatbekov ShymkentKazakhstan Kazakh National Women’s Teacher Training University AlmatyKazakhstan L.N.Gumilyov Eurasian National University Nur-SultanKazakhstan Al-Farabi Kazakh National University AlmatyKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan South Kazakhstan State Pedagogical University ShymkentKazakhstan Asfendiyarov Kazakh National Medical University AlmatyKazakhstan International Taraz Innovative Institute TarazKazakhstan

Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road ***,various methods of photo and video surveillance are used to monitor the condition of the road *** manual approach to evaluation and analysis of the received data can take a protracted period of ***,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 *** 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 *** 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 *** the software implementation of the proposed architecture,the Python programming language and the TensorFlow framework were *** use of the proposed model is effective even in conditions of a limited amount of source *** as a result of experiments,a high degree of repeatability of the results was *** 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.

关键词： Road damage mask R-CNN deep learning detection segmentation

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