Cargando…
Deep Multimodal Detection in Reduced Visibility Using Thermal Depth Estimation for Autonomous Driving
Recently, the rapid development of convolutional neural networks (CNN) has consistently improved object detection performance using CNN and has naturally been implemented in autonomous driving due to its operational potential in real-time. Detecting moving targets to realize autonomous driving is an...
Autores principales: | Yoon, Sungan, Cho, Jeongho |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316778/ https://www.ncbi.nlm.nih.gov/pubmed/35890766 http://dx.doi.org/10.3390/s22145084 |
Ejemplares similares
-
SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads without Lane Lines
por: Palafox, Pablo R., et al.
Publicado: (2019) -
Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation
por: Garillos-Manliguez, Cinmayii A., et al.
Publicado: (2021) -
Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility
por: Shopovska, Ivana, et al.
Publicado: (2019) -
Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications
por: El-Yabroudi, Mohammad Z., et al.
Publicado: (2022) -
Real-time depth completion based on LiDAR-stereo for autonomous driving
por: Wei, Ming, et al.
Publicado: (2023)