Abstract:
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.
