Development of an inspection system for cracks in a concrete tunnel lining

2007 ◽  
Vol 34 (8) ◽  
pp. 966-975 ◽  
Author(s):  
Seung Yeol Lee ◽  
Sang Ho Lee ◽  
Dong Ik Shin ◽  
Young Kap Son ◽  
Chang Soo Han
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Tunnel Lining ◽  
Inspection System ◽  
Visual Methods ◽  
Charge Coupled Device ◽  
Nondestructive Tests ◽  
Series Of Experiments ◽  
Analysis System ◽  
Periodic Inspections

Over the last several decades, many concrete tunnels have been constructed for roads, highways, and railways. For safety in concrete tunnels, periodic inspections have been conducted using nondestructive testing technologies and techniques. However, nondestructive tests cannot replace visual inspection because of their slow and complicated procedures. For this reason, their use has been limited to precision inspections. Visual methods of assessment also require significant time commitments, and they produce subjective results regarding measured crack data. This study proposes an inspection system for the rapid measurement of cracks in tunnel linings and provides an objective method for assessing crack data for safety purposes. The system consists of both image data acquisition and analysis systems. The acquisition system takes images with charge-coupled device (CCD) line-scan cameras. The analysis system extracts crack information from the acquired images using image processing. Measured crack information includes the thickness, length, and orientation of cracks. To improve the accuracy of crack recognition, the geometric properties and patterns of cracks in concrete structures should be applied to image processing. This proposed system was verified through a series of experiments in both laboratory and field environments. Key words: crack, inspection, image processing, tunnel lining, tunnel safety.

scholarly journals Image Processing for Sustainable Remodeling: Introduction to Real-time Quality Inspection System of External Wall Insulation Works

2019 ◽  
Vol 11 (4) ◽  
pp. 1081 ◽  
Author(s):  
Sang-Ho Cho ◽  
Kyung-Tae Lee ◽  
Se-Heon Kim ◽  
Ju-Hyung Kim
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Image Data ◽  
Inspection System ◽  
Quality Inspection ◽  
Labor Costs ◽  
External Wall ◽  
Graphic Data ◽  
Repair Costs ◽  
Serious Injury

The external wall insulation method was introduced to enhance the energy efficiency of existing buildings. It does not cause a decrease of inner space and costs less in comparison to methods that insert insulation panels inside walls. However, it has been reported that external wall insulation boards are disconnecting from walls due to malfunctions of the adhesive. This causes not only repair costs, but also serious injury to pedestrians. Separation problems occur when the bonded positions are incorrect and/or the total area and thickness of the adhesive is smaller than the required amount. A challenge is that these faults can hardly be inspected after installing boards. For this reason, a real-time inspection system is necessary to detect potential failure during adhesive works. Position, area and thickness are major aspects to inspect, and thus a method to process image data of these seems efficient. This paper presents a real-time quality inspection system introducing image processing technology to detect potential errors during adhesive works of external wall insulation, and it is predicted to contribute to achieving sustainable remodeling construction by reducing squandered material and labor costs. The system consists of a graphic data creation module to capture the results of adhesive works and a quality inspection module to judge the pass or fail of works according to an algorithm. A prototype is developed and validated against 100 panels with 800 adhesive points.

Automatic Inspection System for Grain Size Distribution Using a Commercial Grind Gauge

2008 ◽  
Vol 381-382 ◽  
pp. 323-324
Author(s):  
M. Yoshida ◽  
Kazuhisa Yanagi ◽  
M.H. Hafiz ◽  
M. Hara
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Ccd Camera ◽  
Inspection System ◽  
Automatic Inspection ◽  
Image Processing Algorithm ◽  
Measurement Results ◽  
Optical Configuration ◽  
Processing Techniques ◽  
Measuring Tool

Grind gauge is a measuring tool for size of grains or particles included in paint or ink. Its geometrical specifications and operational procedure are regulated to some extent and partly standardized in both ISO and JIS. However, only skilled technician can manage to handle it properly and to obtain correct measurement results. The objective of this study is to develop an automatic inspection system for the grain or particle size by use of artificial lighting and CCD camera with image processing techniques. A telecentric lens system was constructed and high resolution CCD camera was attached to it. Advantages of coaxial illumination and oblique illumination methods were revealed and their applicability was examined respectively. The optical configuration to cover the scale and the whole groove width of grind gauge was devised so that the captured image data could contain both grain/particle distribution and height location. A proper software program followed by image processing algorithm was established to reveal particle mark and liner mark.

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

A Hair Volume Analysis System Using Deep Learning and Image Processing Technologies

2021 ◽  
Author(s):  
Ming-Che Chen ◽  
Wan-Jung Chang ◽  
Yu-Xiang Xiao ◽  
Zi-Xuan Zhang ◽  
Yi-Chan Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Processing Technologies ◽  
Volume Analysis ◽  
Analysis System

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

EDIBLE BIRD NEST SHAPE INSPECTION USING FOURIER DESCRIPTOR (FD) AND FARTHEST FOURIER POINT SIGNATURE (FFPS) METHOD

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
F. S. A. Sa’ad ◽  
M. F. Ibrahim ◽  
A. Y. M. Shakaff ◽  
A. Zakaria
Keyword(s):  
Hybrid Method ◽  
Vision System ◽  
Image Data ◽  
Fourier Descriptor ◽  
Data Set ◽  
Charge Coupled Device ◽  
Bird Nest ◽  
Wilks Lambda ◽  
Shape Inspection ◽  
Shape And Size

Swiftlets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. To date, the bird nest grading is based on weight, shape and size. Current inspection and grading for raw, edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments and often biased. A novel hybrid method from Fourier Descriptor (FD) method and Farthest Fourier Point Signature (FFPS) was developed using Charge Coupled Device (CCD) image data to grade bird nest by its shape and size. From the result, the hybrid method was able to differentiate different shape such as super AAA, super and corner grade depending on the Swiftlet species and geographical origin. The Wilks' lambda analysis was invoked to transform and compress the data set comprising of a large number of interconnected variables to a reduced set of varieties. Overall, the vision system was able to correctly classify 92.6 % of the super AAA, super and Corner shaped grades using the combined FD and FFPS features.

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