Abstract
The improvement of road system quality is a critical task. The mechanism to address such important issue is close monitoring of road pavement condition. Traditional approach requires manual identification of damages. Taking into account considerable length of road system it is essential to create an effective automatic pavement defects detection tool. This approach will extremely reduce time for monitoring of current road state. In this paper global experience in solution of detection issues of road pavement’s distress is reviewed. The article includes information about the existing datasets of road defects, which are commonly used for detection and segmentation. The present work is based on deep learning approach with the use of synthetic generated training data for segmentation of cracks in driver-view image. The novelty of the approach lies in creating synthetic dataset for training state-of-the-art deep learning frameworks. The relevance of the research is emphasized by processing of wide-view images in which heterogeneous pixel intensity, complex crack topology, different illumination condition and complexity of background make the task challenging.
Highlights
Infrastructure in modern economic model is a sufficiently important key point
We propose algorithm for generation instance-level synthetic dataset for crack segmentation based on well-known collections with a marked road, such as KITTI and Cityscapes dataset
ResNet101 with Feature pyramid networks (FPN) forms backbone for feature maps construction
Summary
Infrastructure in modern economic model is a sufficiently important key point. From the perspective of Russian Government statistical data analyses the 67.1% of all freights in 2018 were transported by road. In recent years the volume of carriage steadily increase as well as the amount of private vehicles. All of these factors considerably influence the road pavement condition. Deterioration of road system in turn lead to decrease in shipment transport speed and increase the number of road accidents. This is precisely why the task of road system quality monitoring is practically essential
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