Results 281 to 290 of about 1,475,288 (355)

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Comput. Aided Civ. Infrastructure Eng., 2020
Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack
Jingwei Liu, Xu Yang, Stephen L. H. Lau, Xin Wang, S. Luo, V. C. Lee, Ling Ding   +6 more
semanticscholar   +1 more source

Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning

, 2020
This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions.
Dongho Kang, Sukhpreet S. Benipal, Dharshan Lokekere Gopal, Y. Cha   +3 more
semanticscholar   +1 more source

Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction

Comput. Aided Civ. Infrastructure Eng., 2020
Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation infrastructures.
Yu-Fei Liu, X. Nie, Jiangsheng Fan, Xiao-Gang Liu   +3 more
semanticscholar   +1 more source

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Comput. Aided Civ. Infrastructure Eng., 2018
The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming.
Xincong Yang, Heng Li, Yantao Yu, Xiaochun Luo, Ting Huang, Xu Yang   +5 more
semanticscholar   +1 more source

An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement

Automation in Construction, 2020
Discovering and assessing cracks is widely thought to be critical for maintaining the healthy conditions of asphalt pavement. Unfortunately, the inspection of pavement for cracks is not only labor-intensive, time-consuming, inefficient, and costly, but ...
Ankang Ji, X. Xue, Yuna Wang, Xiaowei Luo, Weirui Xue   +4 more
semanticscholar   +1 more source

Cracks and Crack Control in Concrete Structures

PCI Journal, 1988
T he material presented in this paper is based on more than 30 years of research, observations and experience concerning causes, control, and consequences of cracking in concrete structures. This extensive background was helpful in the preparation of this paper which deals with questions of concrete cracking.
openaire   +1 more source

Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete

Construction and Building Materials, 2018
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures.
S. Dorafshan, R. Thomas, Marc Maguire
semanticscholar   +1 more source

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Comput. Aided Civ. Infrastructure Eng., 2019
Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure.
Seongdeok Bang, Somin Park, Hongjo Kim, Hyoung-Ro Kim   +3 more
semanticscholar   +1 more source

Cracking the problem of cracking cathodes

Science, 2020
Batteries Polycrystalline cathode materials that contain a combination of nickel, manganese, and cobalt have been used for advanced lithium batteries. These materials fracture at high voltage, which increases surface area and leads to more side reactions and shorter cycle life. Using single-crystalline samples as model materials, Bi et al.
openaire   +1 more source

Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system

Comput. Aided Civ. Infrastructure Eng., 2019
Crack information provides important evidence of structural degradation and safety in civil structures. Existing inspection methods are inefficient and difficult to rapidly deploy.
Shang Jiang, Jian Zhang
semanticscholar   +1 more source