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Shuffle Attention-Based Pavement-Sealed Crack Distress Detection

Sensors
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 components: the feature extraction network, the detection head, and the Wise Intersection over Union loss ...
Bo Yuan +4 more
openaire +3 more sources

Characterization of Alaskan Hot-Mix Asphalt containing Reclaimed Asphalt Pavement Material [PDF]

, 2016
In order to properly characterize Alaskan HMA materials containing RAP, this study evaluated properties of 3 asphalt binders typically used in Alaska, PG 52-28, PG 52-40, and PG 58-34, and 11 HMA mixtures containing up to 35% RAP that were either ...
Li, Lin, Liu, Jenny, Zhao, Sheng
core

Friction material (metal reinforcement) analysis of brake pad for light rail train system [PDF]

, 2016
Brake friction material is very important in braking system where they convert kinetic energy of moving vehicles to thermal energy by friction during braking process. The purpose of this research is to determine the optimal friction materials composition
Abdul Rahman, Abdul Rashid
core

Development of mechanistic-empirical distress prediction model for the perfomance of superpave designed asphalt pavement using simplified dynamic modulus analysis method / Nuryantizpura Mohamad Rais [PDF]

, 2016
The permanent deformation becomes a major problem as axle loading increase where it normally happens on highly stressed roads. Permanent deformation always is a potential major distress where the ruts trap water and cause hydroplaning.
Mohamad Rais, Nuryantizpura
core

PaveDistress: A comprehensive dataset of pavement distresses detection

Data in Brief
The PaveDistress dataset contains high-resolution images of road surface distresses, including cracks, repairs, potholes, and background images without defects. The data were collected using a specialized pavement inspection vehicle along the S315 highway in China.
Zhen Liu, Wenxiu Wu, Xingyu Gu, Bingyan Cui +3 more
openaire +2 more sources

Deep spatial attention networks for vision-based pavement distress perception in autonomous driving. [PDF]

PLoS One
Deng F, Jin J.
europepmc +1 more source

Deep Metric Learning-Based Classification for Pavement Distress Images. [PDF]

Sensors (Basel)
Li Y, Wang J, Lü B, Yang H, Wu X.
europepmc +1 more source

Attain: Inclusive annotated pavement distress types and severity dataset. [PDF]