2026-05-28T19:50:05Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/330432026-02-03T12:18:08Zcom_10630_2254col_10630_37959

Detección de participantes del tráfico en entornos urbanos sobre imágenes RGB y nubes de puntos 3D. Montenegro, Jorge García-Guillén, Alejandro Castro Payán, Francisco Manuel Martínez-Rodríguez, Jorge Luis Morales-Rodríguez, Jesús Vehículos autodirigidos Detectores Redes neuronales (Informática) Tráfico - Regulación - Métodos de simulación This article proposes the development of a test environment for the detection of traffic participants in urban environments using neural networks based on the processing of data from vehicle sensors: an RGB camera and a 3D LiDAR sensor. It presents the integration of the realistic simulator CARLA (Car Learning to Act), which allows the detailed recreation of complex urban scenarios, together with ROS2 (Robot Operating System), which is a framework for the development of robotic applications. Specifically, for the case of RGB images, the performance of the CNN (Convolutional Neural Network) YOLOv8 and the DETR (Detection Transformer) is qualitatively evaluated. Similarly, for the detection of traffic participants in point clouds, the PV- RCNN (PointVoxel Regional based Convolutional Neural Network) and its evolution Part-A2-Net are analysed. 2024-09-24T11:22:46Z 2024-09-24T11:22:46Z 2024-09-24T11:22:46Z 2024 conference output Montenegro, J., Garc´ıa-Guillen, ´ A., Castro, F.M., Mart´ınez, J.L., Morales, J. 2024. Detection of Traffic Participants in Urban Environments from RGB images and 3D point clouds Jornadas de Autom´atica, 45. https://doi.org/10.17979/ja-cea.2024.45.10870 https://hdl.handle.net/10630/33043 spa Jornadas de Automática Málaga, España 4-6 septiembre de 2024 http://creativecommons.org/licenses/by-nc-sa/4.0/ open access Attribution-NonCommercial-ShareAlike 4.0 Internacional Comité Español de Automática