scholarly journals Crack Detection on a Retaining Wall with an Innovative, Ensemble Learning Method in a Dynamic Imaging System

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4784 ◽  
Author(s):  
Chern-Sheng Lin ◽  
Shih-Hua Chen ◽  
Che-Ming Chang ◽  
Tsu-Wang Shen
Keyword(s):  
Ensemble Learning ◽  
Crack Detection ◽  
Imaging System ◽  
Retaining Wall ◽  
Local Binary Patterns ◽  
Dynamic Imaging ◽  
Learning Method ◽  
Unmanned Vehicle ◽  
Feature Values ◽  
A Charge

In this study, an innovative, ensemble learning method in a dynamic imaging system of an unmanned vehicle is presented. The feasibility of the system was tested in the crack detection of a retaining wall in a climbing area or a mountain road. The unmanned vehicle can provide a lightweight and remote cruise routine with a Geographic Information System sensor, a Gyro sensor, and a charge-coupled device camera. The crack was the target to be tested, and the retaining wall was patrolled through the drone flight path setting, and then the horizontal image was instantly returned by using the wireless transmission of the system. That is based on the cascade classifier, and the feature comparison classifier was designed further, and then the machine vision correlation algorithm was used to analyze the target type information. First, the system collects the target image and background to establish the samples database, and then uses the Local Binary Patterns feature extraction algorithm to extract the feature values for classification. When the first stage classification is completed, the classification results are target features, and edge feature comparisons. The innovative ensemble learning classifier was used to analyze the image and determine the location of the crack for risk assessment.

Interpretable Multi-modal Stacking-based Ensemble Learning Method for Real Estate Appraisal

2021 ◽  
pp. 1-1
Author(s):  
Sutong Wang ◽  
Jiacheng Zhu ◽  
Yunqiang Yin ◽  
Dujuan Wang ◽  
T.C. Edwin Cheng ◽  
...  
Keyword(s):  
Real Estate ◽  
Ensemble Learning ◽  
Learning Method ◽  
Real Estate Appraisal

scholarly journals Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 612 ◽  
Author(s):  
Jue Hu ◽  
Weiping Xu ◽  
Bin Gao ◽  
Gui Tian ◽  
Yizhe Wang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Pattern Mining ◽  
Learning Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Testing Technology ◽  
Deep Region ◽  
Detection And Localization ◽  
Non Destructive

Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.

A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

2013 ◽  
Vol 22 (04) ◽  
pp. 1350025 ◽  
Author(s):  
BYUNGWOO LEE ◽  
SUNGHA CHOI ◽  
BYONGHWA OH ◽  
JIHOON YANG ◽  
SUNGYONG PARK
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Ensemble Methods ◽  
Feature Space ◽  
Training Data ◽  
Learning Method ◽  
Weighted Voting ◽  
Ensemble Learning Algorithm ◽  
Voting Scheme ◽  
Base Learner

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying an instance, we apply a weighted voting scheme among the classifiers that include the instance in their region. We used 11 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as RBE, bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed other approaches.

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