- Home
- Publications
- Publication Search
- Publication Details
Title
Concrete Cracks Detection Based on FCN with Dilated Convolution
Authors
Keywords
-
Journal
Applied Sciences-Basel
Volume 9, Issue 13, Pages 2686
Publisher
MDPI AG
Online
2019-07-01
DOI
10.3390/app9132686
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- The visual object tracking algorithm research based on adaptive combination kernel
- (2019) Yuantao Chen et al. Journal of Ambient Intelligence and Humanized Computing
- Multi-camera transfer GAN for person re-identification
- (2019) Shuren Zhou et al. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
- In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning
- (2019) Vigneashwara Pandiyan et al. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
- Computer vision-based concrete crack detection using U-net fully convolutional networks
- (2019) Zhenqing Liu et al. AUTOMATION IN CONSTRUCTION
- A Fast Detection Method via Region-Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects
- (2018) Yadong Xue et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion
- (2018) Fu-Chen Chen et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding
- (2018) Nhat-Duc Hoang Advances in Civil Engineering
- Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques
- (2018) Jack C.P. Cheng et al. AUTOMATION IN CONSTRUCTION
- Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network
- (2018) Xincong Yang et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Spatial and semantic convolutional features for robust visual object tracking
- (2018) Jianming Zhang et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks
- (2018) Yundong LI et al. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate
- (2018) Jianming Zhang et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique
- (2018) Byunghyun Kim et al. SENSORS
- Autonomous concrete crack detection using deep fully convolutional neural network
- (2018) Cao Vu Dung et al. AUTOMATION IN CONSTRUCTION
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- (2017) Shaoqing Ren et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Detection and monitoring of flexural cracks in reinforced concrete beams using mounted smart aggregate transducers
- (2017) S Taghavipour et al. Smart Materials and Structures
- The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image
- (2016) Z. Qu et al. IMAGING SCIENCE JOURNAL
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started