scholarly journals Review on Buildings Health Monitoring System by Using Hybrid Data Optimization Based on Machine Learning Algorithm

2021 ◽  
pp. 25-29
Author(s):  
Shweta D Shenmare
Keyword(s):  
Crack Detection ◽  
Learning Algorithm ◽  
Structural Safety ◽  
Adjustment Method ◽  
Building Management ◽  
Otsu Method ◽  
Hybrid Data ◽  
Gray Intensity ◽  
Processing Techniques ◽  
Intelligent Model

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is pains takingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

scholarly journals Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Structural Safety ◽  
Building Structures ◽  
Adjustment Method ◽  
Building Management ◽  
Otsu Method ◽  
Gray Intensity

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

scholarly journals UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2650
Author(s):  
Daegyun Choi ◽  
William Bell ◽  
Donghoon Kim ◽  
Jichul Kim
Keyword(s):  
Visual Information ◽  
Crack Detection ◽  
Image Data ◽  
Image Processing Techniques ◽  
Location Determination ◽  
Aerial Vehicle ◽  
Using Data ◽  
Processing Techniques ◽  
Deep Learning Model ◽  
Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

Crack Detection on Metal Surfaces Using Image Processing Techniques

2021 ◽  
Author(s):  
Megharaj Sonawane ◽  
Aditya Borse ◽  
Hrishikesh Sonawane ◽  
Aashish Mali ◽  
Prachi Rajarapollu
Keyword(s):  
Image Processing ◽  
Metal Surfaces ◽  
Crack Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Cement Pavement Surface Crack Detection Based on Image Processing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu
Keyword(s):  
Image Processing ◽  
Laplace Operator ◽  
Image Recognition ◽  
Surface Crack ◽  
Iteration Method ◽  
Crack Detection ◽  
Binary Image ◽  
Threshold Method ◽  
Otsu Method ◽  
Pavement Crack Detection

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

scholarly journals Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 717 ◽  
Author(s):  
Gang Li ◽  
Biao Ma ◽  
Shuanhai He ◽  
Xueli Ren ◽  
Qiangwei Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Width ◽  
Crack Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Fully Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

AN ADAPTIVE NOISE CANCELER IMPLEMENTED WITH CMOS ANALOG TECHNOLOGY

1996 ◽  
Vol 06 (02) ◽  
pp. 139-154
Author(s):  
GABRIEL GOMEZ ◽  
RAYMOND SIFERD
Keyword(s):  
Finite Impulse Response ◽  
Learning Algorithm ◽  
Test Results ◽  
Static Power ◽  
Analog Implementation ◽  
Adaptive Noise ◽  
On Chip ◽  
Processing Techniques ◽  
Noise Canceler ◽  
Signal Processing Techniques

A fully analog implementation of an adaptive noise canceler is presented, including design, simulation, and test results of the fabricated chip. The prototype chip was fabricated using 2-µ CMOS P-Well technology on a 4.0 mm2 die and uses ±5 V power supplies. The static power dissipation is 276 milliwatts. Analog signal processing techniques are used to realize an adaptive system based upon a finite impulse response (FIR) filter and least mean squares (LMS) adaptive algorithm. The circuit is tested as an adaptive noise canceler, where a signal corrupted by noise is the input. The circuit adaptively converges to cancel the noise to produce an output that is the best LMS estimate of the signal. The circuit could be used for other real-time adaptive filter applications or for realizing an on-chip learning algorithm. The implementation illustrates the advantages of an analog system with no requirements for A/D and D/A converters, reduced size of circuit subsystems (e.g. multipliers), and the relatively fast convergence.

scholarly journals Identification of Mimo dynamic system using inverse Mimo Neural Narx model

2013 ◽  
Vol 16 (2) ◽  
pp. 13-25
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Artificial Muscle ◽  
Training Data ◽  
The Novel ◽  
Robot Arm ◽  
Dynamic Inverse ◽  
Intelligent Model ◽  
First Time ◽  
Narx Model

This paper investigates the application of proposed neural MIMO NARX model to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm as to improve its performance in modeling and identification. The contact force variations and nonlinear coupling effects of both joints of the 2-axes PAM robot arm are modeled thoroughly through the novel dynamic inverse neural MIMO NARX model exploiting experimental input-output training data. For the first time, the dynamic neural inverse MIMO NARX Model of the 2-axes PAM robot arm has been investigated. The results show that this proposed dynamic intelligent model trained by Back Propagation learning algorithm yields both of good performance and accuracy. The novel dynamic neural MIMO NARX model proves efficient for modeling and identification not only the 2-axes PAM robot arm but also other nonlinear dynamic systems.

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