Cargando…
3D Object Detection with SLS-Fusion Network in Foggy Weather Conditions
The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to ma...
Autores principales: | Mai, Nguyen Anh Minh, Duthon, Pierre, Khoudour, Louahdi, Crouzil, Alain, Velastin, Sergio A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540926/ https://www.ncbi.nlm.nih.gov/pubmed/34695925 http://dx.doi.org/10.3390/s21206711 |
Ejemplares similares
-
3D Object Detection for Self-Driving Cars Using Video and LiDAR: An Ablation Study
por: Salmane, Pascal Housam, et al.
Publicado: (2023) -
Analysis of Thermal Imaging Performance under Extreme Foggy Conditions: Applications to Autonomous Driving
por: Rivera Velázquez, Josué Manuel, et al.
Publicado: (2022) -
Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks †
por: Pham, Huy Hieu, et al.
Publicado: (2019) -
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
por: Pham, Huy Hieu, et al.
Publicado: (2020) -
SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions
por: Ben-Daoued, Amine, et al.
Publicado: (2023)