METHOD FOR CALCULATING ROAD SURFACES DAMAGE ON BORDER SECTIONS USING COMPUTER VISION
DOI:
https://doi.org/10.28925/2663-4023.2025.31.1025Keywords:
computer vision, artificial intelligence, neural network, ROI, road surface, pothole, damage assessment, YOLOv8Abstract
Within the framework of the conducted research, an analysis and comparison of modern approaches and technological solutions aimed at assessing the condition of transport routes, road surfaces and roads of border areas was carried out. The aim of the work is to improve and optimize the processes related to monitoring, diagnostics and planning of restoration works to increase the efficiency of the relevant services. The international experience of determining the technical condition of roads, methods for calculating quality indicators was analyzed, and the use of innovative technologies that ensure increased accuracy of assessment and automation of data processing was also investigated. An own concept of a system for detecting road damage, calculating consumables and volumes of work at the preparatory stage was proposed. The system is based on the application of artificial intelligence and computer vision technologies using methods for analyzing images and video materials obtained using a video recorder. A specialized neural network training algorithm based on the modern YOLOv8 model has been implemented, which provides automatic detection of defects on the road surface and their quantitative assessment for further determination of the extent of damage, required resources and boundaries of deformation areas. A system for organizing and storing results in local or cloud databases has been proposed, which contributes to effective management of information flows. The developed approach has broad prospects for application in assessing the state of border zones of any state for the purpose of defense, analytical monitoring and quick decision-making. In addition, the improved methodology can be used to automate monitoring of the state of rock, dirt and gravel roads, which will significantly increase the efficiency and quality of repair work in rural, mountainous and border areas.
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Copyright (c) 2025 Дмитро Гика, Ольга Павлова, Микола Лисий
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