Cargando…
Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8
For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to in...
Autores principales: | Kumar, Debasis, Muhammad, Naveed |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611033/ https://www.ncbi.nlm.nih.gov/pubmed/37896564 http://dx.doi.org/10.3390/s23208471 |
Ejemplares similares
-
A Study on Object Detection Performance of YOLOv4 for Autonomous Driving of Tram
por: Woo, Joo, et al.
Publicado: (2022) -
YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios
por: Meng, Xianglin, et al.
Publicado: (2023) -
Fast and accurate object detector for autonomous driving based on improved YOLOv5
por: Jia, Xiang, et al.
Publicado: (2023) -
Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5
por: Yao, Jiale, et al.
Publicado: (2022) -
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
por: Nepal, Upesh, et al.
Publicado: (2022)