Results 71 to 80 of about 9,751 (137)

PY-CrackDB: A pavement crack dataset from paraguayan roads for context-aware computer vision models. [PDF]

Data Brief
Ramírez-Villanueva FG, Vázquez Noguera JL, Legal-Ayala H, Mello-Román JC, Pérez-Estigarribia PE.   +4 more
europepmc   +1 more source

A Physics-Guided Quantitative GPR Framework for Detecting Hanging Sleepers in Ballasted Railway Tracks. [PDF]

Sensors (Basel)
Yang W, Gao J, Xu Z.
europepmc   +1 more source

Clarifying the Quadrennial Needs Study Process, December 1993 [PDF]

, 1993

core  

Prediction of pavement performance via smatrphone vibration-induced unevenness signature using machine learning. [PDF]

Sci Rep
Wasiq S, Golroo A, Amani MJ, Hajikarimi P.   +3 more
europepmc   +1 more source

UNHSC Design Specifications for Porous Asphalt Pavement and Infiltration Beds [PDF]

, 2016
Ballestero, Thomas P., Briggs, Joshua F., Houle, James J., Pochily, Jeff, Puls, Timothy A., Roseen, Robert M., Swenson, Grant   +6 more
core   +1 more source

A lightweight YOLO11n seg framework for real time surface crack detection with segmentation. [PDF]

Sci Rep
Tiwari S, Gola KK, Kanauzia R, Gupta GK.
europepmc   +1 more source

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

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

An intelligent YOLO and CNN-BiGRU framework for road infrastructure based anomaly assessment. [PDF]

Sci Rep
Zhumadillayeva A, Ahanger TA, Matkarimov B.   +2 more
europepmc   +1 more source

Texture-based image analysis and explainable machine learning for polished asphalt identification in pavement condition monitoring. [PDF]

Sci Rep
Fakhri M, Pourjafar SV, Daneshvari MH.
europepmc   +1 more source

Crack detection in structural images using a hybrid Swin Transformer and enhanced features representation block. [PDF]

Front Artif Intell
Anusha N, Anbarasi LJ.
europepmc   +1 more source