@inproceedings{06d39a18651640619ee7f9cb34a64f1b,
title = "Including Transfer Learning and Synthetic Data in a Training Process of a 2D Object Detector for Autonomous Driving",
abstract = "Nowadays the use of deep learning (DL) based systems is widely extended in several areas such as facial recognition, voice and audio processing or perception systems. The training process that must be performed for proper functionality requires a large amount of data with the required characteristics needed for the task to be executed. The process of obtaining new adequate training data is complex and tedious, therefore multiple techniques such as data augmentation or transfer learning have been developed in order to have a greater amount of knowledge in the network without the need to search for new data sources. The aim of this paper is to study the effect of the inclusion of knowledge from transfer learning in a 2D image detector trained with real world and synthetic data from multiple sources. The detector that is going to be trained will be focused on autonomous driving tasks, therefore we decide to use KITTI as the real world data source and our AD PerDevkit (based on CARLA) and Virtual-KITTI as the synthetic sources.",
keywords = "2D object detection, CARLA, Deep learning, KITTI, Simulation, Transfer learning",
author = "Miguel Antunes and Bergasa, \{Luis M.\} and Javier Araluce and Rodrigo Guti{\'e}rrez and Arango, \{J. Felipe\} and Manuel Oca{\~n}a",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th Iberian Robotics Conference, ROBOT 2022 ; Conference date: 23-11-2022 Through 25-11-2022",
year = "2023",
doi = "10.1007/978-3-031-21062-4\_38",
language = "English",
isbn = "9783031210617",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "465--478",
editor = "Danilo Tardioli and Vicente Matell{\'a}n and Guillermo Heredia and Silva, \{Manuel F.\} and Lino Marques",
booktitle = "ROBOT 2022",
address = "Germany",
}