Automatic recognition system for traffic signs in Ecuador based on faster R-CNN with ZFNet

dc.creatorZabala-Blanco, David
dc.creatorAldás, Milton
dc.creatorRomán, Wilson
dc.creatorGallegos, Joselyn
dc.creatorFlores-Calero, Marco
dc.date2023-01-04T19:25:59Z
dc.date2023-01-04T19:25:59Z
dc.date2022
dc.date.accessioned2024-05-02T20:30:19Z
dc.date.available2024-05-02T20:30:19Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/4347
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9274597
dc.descriptionThis research presents an application of the Deep Learning technology in the development of an automatic system detection of traffic signs of Ecuador. The development of this work has been divided into two parts, i) in first a database was built with regulatory and preventive traffic signs, taken in urban environments from several cities in Ecuador. The dataset consists of 52 classes, collected in the various lighting environments (dawn, day, sunset and cloudy) from 6 am to 7 pm, in various localities of Ecuador, ii) then, an object detector based on Faster-RCNN with ZF-Net was implemented as a detection/recognition module. The entire experimental part was developed on the ViiA technology platform, which consists of a vehicle for the implementation of driving assistance systems using Computer Vision and Artificial Intelligence, in real road driving conditions.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceCommunications in Computer and Information Science, 1675, 44-57
dc.subjectDeep learning
dc.subjectTraffic accidents
dc.subjectTraffic signs
dc.subjectEcuador
dc.subjectFaster R-CNN
dc.subjectZF-Net
dc.subjectComputer vision
dc.titleAutomatic recognition system for traffic signs in Ecuador based on faster R-CNN with ZFNet
dc.typeArticle


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