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Automatic recognition system for traffic signs in Ecuador based on faster R-CNN with ZFNet
| dc.contributor.author | Zabala-Blanco, David | |
| dc.contributor.author | Aldás, Milton | |
| dc.contributor.author | Román, Wilson | |
| dc.contributor.author | Gallegos, Joselyn | |
| dc.contributor.author | Flores-Calero, Marco | |
| dc.date.accessioned | 2023-01-04T19:25:59Z | |
| dc.date.available | 2023-01-04T19:25:59Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/4347 | |
| dc.description.abstract | This 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. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Communications in Computer and Information Science, 1675, 44-57 | es_CL |
| dc.subject | Deep learning | es_CL |
| dc.subject | Traffic accidents | es_CL |
| dc.subject | Traffic signs | es_CL |
| dc.subject | Ecuador | es_CL |
| dc.subject | Faster R-CNN | es_CL |
| dc.subject | ZF-Net | es_CL |
| dc.subject | Computer vision | es_CL |
| dc.title | Automatic recognition system for traffic signs in Ecuador based on faster R-CNN with ZFNet | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | link.springer.com/chapter/10.1007/978-3-031-20319-0_4 | es_CL |
| dc.ucm.doi | doi.org/10.1007/978-3-031-20319-0_4 | es_CL |
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