Zobrazeno 1 - 10
of 976 379
pro vyhledávání: '"Weather conditions."'
Autor:
Karvat, Mateus, Givigi, Sidney
Adverse weather conditions pose a significant challenge to the widespread adoption of Autonomous Vehicles (AVs) by impacting sensors like LiDARs and cameras. Even though Collaborative Perception (CP) improves AV perception in difficult conditions, ex
Externí odkaz:
http://arxiv.org/abs/2410.06380
Autor:
Shaik, Furqan Ahmed, Nagar, Sandeep, Maturi, Aiswarya, Sankhla, Harshit Kumar, Ghosh, Dibyendu, Majumdar, Anshuman, Vidapanakal, Srikanth, Chaudhary, Kunal, Manchanda, Sunny, Varma, Girish
The ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions served as a rigorous platform to evaluate and benchmark state-of-the-art semantic segmentation models under challenging conditions f
Externí odkaz:
http://arxiv.org/abs/2409.05327
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2024
The visible-light camera, which is capable of environment perception and navigation assistance, has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems (IWTS). However, the visual imaging
Externí odkaz:
http://arxiv.org/abs/2409.01500
With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environment
Externí odkaz:
http://arxiv.org/abs/2410.17734
Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is crit
Externí odkaz:
http://arxiv.org/abs/2409.18932
Online Domain Adaptation (OnDA) is designed to handle unforeseeable domain changes at minimal cost that occur during the deployment of the model, lacking clear boundaries between the domain, such as sudden weather events. However, existing OnDA metho
Externí odkaz:
http://arxiv.org/abs/2409.01072
Autor:
SHABLIA, Volodymyr1 shabliavladimir@gmail.com, KUNETS, Victoria2, DANILOVA, Tetiana1, SHABLIA, Petro2
Publikováno v:
Scientific Papers. Series D. Animal Science. 2024, Vol. 67 Issue 1, p177-184. 8p.
Autor:
Deymi-Dashtebayaz, Mahdi1,2 (AUTHOR) mahdi.deymi@gmail.com, Tambulatova, Ekaterina1 (AUTHOR), Norani, Marziye2 (AUTHOR), Asadi, Mostafa2 (AUTHOR), Asach, Aleksei1 (AUTHOR)
Publikováno v:
Environment, Development & Sustainability. Nov2024, Vol. 26 Issue 11, p27945-27974. 30p.
Autor:
Neelam, Harish
This paper presents a multilevel hierarchical framework for the classification of weather conditions and hazard prediction. In recent years, the importance of data has grown significantly, with various types like text, numbers, images, audio, and vid
Externí odkaz:
http://arxiv.org/abs/2407.16834
Autor:
Ding, Yuexiong, Luo, Xiaowei
Though current object detection models based on deep learning have achieved excellent results on many conventional benchmark datasets, their performance will dramatically decline on real-world images taken under extreme conditions. Existing methods e
Externí odkaz:
http://arxiv.org/abs/2406.12395