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Article

An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images

1
Expert Systems and Applications Lab—ESALAB, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain
2
Laboratory of Embedded and Distribution Systems, University of Vale do Itajaí, Rua Uruguai 458, C.P. 360, Itajaí 88302-901, Brazil
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(21), 6205; https://doi.org/10.3390/s20216205
Received: 9 September 2020 / Revised: 21 October 2020 / Accepted: 27 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Image Sensors: Systems and Applications)
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. View Full-Text
Keywords: smart applications; drones; YOLOv4; crack detection; virtual organizations of agents smart applications; drones; YOLOv4; crack detection; virtual organizations of agents
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MDPI and ACS Style

Silva, L.A.; Sanchez San Blas, H.; Peral García, D.; Sales Mendes, A.; Villarubia González, G. An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images. Sensors 2020, 20, 6205. https://doi.org/10.3390/s20216205

AMA Style

Silva LA, Sanchez San Blas H, Peral García D, Sales Mendes A, Villarubia González G. An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images. Sensors. 2020; 20(21):6205. https://doi.org/10.3390/s20216205

Chicago/Turabian Style

Silva, Luís A., Héctor Sanchez San Blas, David Peral García, André Sales Mendes, and Gabriel Villarubia González. 2020. "An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images" Sensors 20, no. 21: 6205. https://doi.org/10.3390/s20216205

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