2023
Silva, A. L.; Oliveira, P.; Durães, D.; Fernandes, D.; Névoa, R.; Monteiro, J.; Melo-Pinto, P.; Machado, J.; Novais, P.
Em: Sensors, vol. 23, não 14, 2023, ISSN: 14248220, (cited By 1).
Resumo | Links | BibTeX | Etiquetas: 3D object; 3d object detection; Autonomous driving; Deep learning method; Learning methods; LiDAR sensing technology; Objects detection; Sensing technology; Software Specification; State of the art, article; deep learning; software, Autonomous vehicles; Deep learning; Learning systems; Object recognition; Optical radar; Specifications; Three dimensional computer graphics, Object detection
@article{Silva2023,
title = {A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications},
author = {A. L. Silva and P. Oliveira and D. Durães and D. Fernandes and R. Névoa and J. Monteiro and P. Melo-Pinto and J. Machado and P. Novais},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166008165&doi=10.3390%2fs23146427&partnerID=40&md5=53b52635577fee2589e3dd1eca23b062},
doi = {10.3390/s23146427},
issn = {14248220},
year = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {14},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements. © 2023 by the authors.},
note = {cited By 1},
keywords = {3D object; 3d object detection; Autonomous driving; Deep learning method; Learning methods; LiDAR sensing technology; Objects detection; Sensing technology; Software Specification; State of the art, article; deep learning; software, Autonomous vehicles; Deep learning; Learning systems; Object recognition; Optical radar; Specifications; Three dimensional computer graphics, Object detection},
pubstate = {published},
tppubtype = {article}
}
The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements. © 2023 by the authors.