Zobrazeno 1 - 10
of 241
pro vyhledávání: '"distress detection"'
Publikováno v:
IEEE Access, Vol 12, Pp 105055-105068 (2024)
Pavement distress detection is crucial in road health assessment and monitoring. However, there are still some challenges in extracting pavement distress based on deep learning: such as insufficient segmentation, extraction errors and discontinuities
Externí odkaz:
https://doaj.org/article/3e3f580383b64cc4ac9a4fa6346891e1
Autor:
Tianwen Li, Gongquan Li
Publikováno v:
Sensors, Vol 24, Iss 21, p 6783 (2024)
The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based
Externí odkaz:
https://doaj.org/article/8bc91ba20c1a443ba1518cfcd8766ab6
Publikováno v:
PeerJ Computer Science, Vol 10, p e2038 (2024)
In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappl
Externí odkaz:
https://doaj.org/article/b9396cb9920841aba6ec49f84e6166d0
Publikováno v:
Sensors, Vol 24, Iss 17, p 5757 (2024)
To enhance the detection of pavement-sealed cracks and ensure the long-term stability of pavement performance, a novel approach called the shuffle attention-based pavement-sealed crack detection is proposed. This method consists of three essential co
Externí odkaz:
https://doaj.org/article/9836b980128249faba6ca65835985794
Publikováno v:
Developments in the Built Environment, Vol 17, Iss , Pp 100315- (2024)
Research on road infrastructure structural health monitoring is critical due to the increasing problem of deteriorated conditions. The traditional approach to pavement distress detection relies on human-based visual recognition, a time-consuming and
Externí odkaz:
https://doaj.org/article/b0dbd85b0b084d84845fcb912e5d5195
Publikováno v:
Journal of Engineering Studies and Research, Vol 29, Iss 3 (2024)
Distress occurs when a person is in anxiety or fear. Existing research in distress detection arising from physical attacks focused mainly on the use of machine learning techniques. To extend research efforts, this study proposes an alternate approach
Externí odkaz:
https://doaj.org/article/40cf31cc3bbf4bebb1a4a9649392545a
Publikováno v:
Journal of Traffic and Transportation Engineering (English ed. Online), Vol 10, Iss 2, Pp 276-290 (2023)
To achieve automatic, fast, efficient and high-precision pavement distress classification and detection, road surface distress image classification and detection models based on deep learning are trained. First, a pavement distress image dataset is b
Externí odkaz:
https://doaj.org/article/8934f06eee17450fae7d70e5d8e871ea
Autor:
Haohui Yan, Junfei Zhang
Publikováno v:
Data in Brief, Vol 51, Iss , Pp 109692- (2023)
The UAV-PDD2023 dataset consists of pavement distress images captured by unmanned aerial vehicles (UAVs) in China with more than 11,150 instances under two different weather conditions and across varying levels of construction quality. The roads in t
Externí odkaz:
https://doaj.org/article/572f571fcbd24ffda6c0171db8bb0d46
Publikováno v:
Infrastructures, Vol 9, Iss 2, p 34 (2024)
Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments.
Externí odkaz:
https://doaj.org/article/eb03d1dc027a4af8be7e7d16602492d0
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