RT journal article
T1 A multi-DL fuzzy approach to image recognition for a real-time traffic alert system
A1 Muñoz Ortega, Andrés
A1 Martínez España, Raquel
A1 Guerrero Contreras, Gabriel José
A1 Balderas Díaz, Sara
A1 Arcas Túnez, Francisco
A1 Bueno Crespo, Andrés
A2 Ingeniería Informática
K1 Traffic monitoring
K1 Deep Learning
K1 Fuzzy classification
K1 Alert system
K1 Image recognition
K1 Real-time system
AB This paper presents a novel Multi-DL Fuzzy Approach aimed at performing image recognition in the development of a real-time traffic alert system, addressing the problem of traffic congestion and related incidents. Traditional monitoring by road operators predominantly relies on fixed location cameras, yielding limited and sometimes ambiguous information. This study proposes leveraging Twitter (now known as 'X') as a more comprehensive data source alongside employing fuzzy techniques with Deep Learning (DL) neural networks such as CNN, VGG16, and Xception to analyze and classify traffic images. The innovative integration of these technologies augments the precision in categorizing varying traffic conditions, namely fluid and dense traffic, accidents and fires. Thus, this proposal mitigates the ambiguities prevalent in traffic image interpretation, and reduces the dependency on static data sources. The proposed models showed improved results by combining information from the DL models, elevating accuracy from 84% in crisp classification to 90% utilizing fuzzy information.
PB IOS Press
SN 1876-1364
YR 2024
FD 2024-06-20
LK http://hdl.handle.net/10498/34139
UL http://hdl.handle.net/10498/34139
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 09-nov-2026