Abstract
In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.
Highlights
Road safety is key to reducing the number of road accidents
We aimed to develop a platform capable of monitoring the state of roads and detecting different types of road damage to help coordinate the people and organizations involved in the identification process
We focus on one of these image detection systems, You Only Look Once (YOLO), which is used in our framework, as well as some of the most used datasets
Summary
Road safety is key to reducing the number of road accidents. The main solutions often focus on reducing speed limits, but road conditions are important in the increase in road accidents. A recent study carried out by the AEC stated that the poor condition of roads is the main cause of 94% of accidents [1]. According to this organization, 1 out of every 13 km of the Spanish road network shows significant deterioration in more than 50% of the surface of the pavement as a result of accumulating potholes, ruts, and cracks. The report [1] shows that Spain is failing to maintain its roads, and describes the condition of the asphalt as very poor This translates into an increased risk of an accident in the worst-case scenario, but can lead to vehicle breakdowns. We refer to the framework in which we include the proposed system, the multi-agent system, highlighting its major advances and utilities
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