Abstract
Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.
Published Version
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