Crack Detection System Research Articles

Efficient Road Crack Detection Based on an Adaptive Pixel-Level Segmentation Algorithm

Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types. This paper proposes a new method that uses an adapted version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. The method uses the Gaussian cumulative density function (CDF) as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of various pavement noise conditions. The method proved to be time and cost-efficient as it took less than 3.15 s per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to generate the detection results. This makes the proposed method a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of medium to severe-level cracks (precision = 79.21%, recall = 89.18%, and F1 score = 83.90%).

Efficient model for IoT based railway crack detection system

Abstract In present’s world, transport is a key requirement. The fourth biggest railway set of connections in India is the human race. This paper discuss about the detection of crack in a railway track. In previous methods GPS module and the GSM modem were used. It leads to high cost. The effective railway crack detection system utilizing the simple components inclusive of a RF transmitter and receiver, LED –LDR set up. It has low cost compared to the existing techniques. In this paper LED and LDR assembly is utilized to find out the crack in a railway track. RF is an obvious option for message, because it allows more information to be transferred at high speed and over long remoteness. Here the sensor data is transferred to control room or monitoring unit. In this paper we proposed an IoT Based crack detection system using LED-LDR assembly, RF transceiver include autonomous power unit using solar powered battery.

A Development of Road Crack Detection System Using Deep Learning-based Segmentation and Object Detection

A Development of Road Crack Detection System Using Deep Learning-based Segmentation and Object Detection

A CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK

The quickly expanded development of artificial intelligence offers alternative ways to solve numerous civil engineering problems. The work is devoted to the development of a computer-vision-based crack detection system capable to process big data related to pathology recognition. In this study, we discuss an automated crack type classification pipeline based on CNN deep learning algorithm and MapReduce framework. The results of numerical modeling illustrate the potential of the crack detection system.

Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

This paper presents an image-based crack detection system, in which its architecture is modified to use deep convolutional neural networks in a feature extraction step and other classifiers in the classification step. In the classification step, classifiers including Support Vector machines (SVMs), Random Forest (RF) and Evolutionary Artificial Neural Network (EANN) are used as an alternative to a Softmax classifier and the performance of these classifiers are studied. The data set was created from various types of concrete structures using a standard digital camera and an unmanned aerial vehicle (UAV). The collected images are used in the crack detection system and in creating a 3D model of a sample concrete building using an image- based 3D photogrammetry technique. Then, the 3D model is used to create a mosaic image, in which the crack detection system was applied to create a global view of a crack density map. The map is then projected onto the 3D model to allow cracks to be located in the 3D world. A comparative study was conducted on the proposed crack detection system and the results prove that the combined architecture of CNN as a feature extractor and SVM as a classifier shows the best performance with the accuracy of 92.80. The results also show that the modified architecture by integrating CNN and other types of classifiers can improve a system performance, which is better than using the Softmax classifier.

Experimental issues in the instrumented 3 point bending VDA238-100 test

The instrumented 3-point bending test according to VDA238-100 test standard is increasingly used within the steel and automotive industry. Originally developed for aluminum hemming characterization, this bending test has been shown to be also relevant for local formability and crash foldability assessment originally of press-hardened steel grades and more recently for newly developed advanced and ultra high strength AHSS/UHSS steels grades. This instrumented bending test delivers bending load vs. bending angle curves. It is commonly assumed that material failure shortly happens beyond maximum load after a 30N load drop. The bending angle at maximum force aFmax characterizes then the bendability of the investigated material. The assumption maximum force = bending crack initiation, while being true for press-hardened grades, is in too many case not valid for steel grades with tensile strength ⩽1200MPa and cannot be universally trusted. An alternative approach is presented using passive acoustic emission sensors placed in the vicinity of the bending punch. The interpretation of such acoustic data is however quite subjective and still in trial status. Redundant crack detection systems based on load, acoustic as well as optical measurements may have to be considered together for increasing crack detection reliability within the VDA238-100 bending test specification.

SENSOR EVALUATION FOR CRACK DETECTION IN CONCRETE BRIDGES

Abstract. Bridges are one of the most critical traffic infrastructure objects, therefore it is necessary to monitor them at regular intervals. Nowadays, this monitoring is made manually by visual inspection. In recent projects, the authors are developing automated crack detection systems to support the inspector. In this pre-study, different sensors, like different camera systems for photogrammetry, a laser scanner, and a laser triangulation system are evaluated for crack detection based on a defined required minimum crack width of 0.2 mm. The used test object is a blasted concrete plate, sized 70 cm × 70 cm × 5 cm and placed in an outdoor environment. The results of the data acquisition with the different sensors are point clouds, which make the results comparable. The point cloud from the chosen laser scanner is not sufficient for the required crack width even at a low speed of 1 m/s. The RGB or intensity information of the photogrammetric point clouds, even based on a low-cost smartphone camera, contain the targeted cracks. The authors advise against using only the 3D information of the photogrammetric point clouds for crack detection due to noise. The laser triangulation system delivers the best results in both intensity and 3D information. The low weight of camera systems makes photogrammetry to the preferred method for an unmanned aerial vehicle (UAV). In the future, the authors aim for crack detection based on the 2D images, automated by using machine learning, and crack localisation by using structure from motion (SfM) or a positioning system.

Application of neural network method for road crack detection

The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.

Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

Temperature tracer method for crack detection in underwater concrete structures

Crack detection is an important issue in the health monitoring and performance evaluation of concrete structures. Based on the heat transfer theory, a temperature tracer method and a monitoring system for crack detection in underwater concrete structures are presented in this paper. The system is composed of an integrated sensing and heating system, a monitoring tube, and multiple casing tubes. The inner diameter of the casing tube should be larger than the outer diameter of the monitoring tube so that there is a certain gap between the casing tube and the monitoring tube to form a cavity. Water fills the cavity along the crack face when the underwater concrete structure cracks, resulting in a change in the surrounding medium at the cracked site. Thus, the cooling rate of the heat source in the monitoring tube corresponding to the cracked site will be accelerated. Crack identification can be done based on a comparative analysis of the cooling curves of the heat source in the uncracked and cracked states. The effects of cavity geometry size and intensity of heat source on crack identification are studied using numerical simulations and crack detection tests. Both simulations and test results confirm the feasibility of the proposed crack detection method.

Comparison of Inline Crack Detection Systems for Multicrystalline Silicon Solar Cells

Cracks in silicon solar cells reduce cell efficiency and may lead to critical failures in cell and module production because of breakage. Detecting cracks in an early production stage is, therefore, advisable. Several approaches for crack detection systems have been developed in the past years. In order to compare the system performance, the rating done by the system has to be compared with a reference, i.e., a ground truth defining the crack's presence, position, and/or area. This information can also be used for training of detection algorithms based on supervised learning, e.g., convolutional neural networks. In the present article, we show that finding the ground truth is ambiguous and depends strongly on the operator and the applied metrology. For this end, we apply six crack detection tools to a set of 120 multicrystalline silicon passivated emitter and rear cells that were sorted out in industrial production because of cracks, and compare the human rating of six operators for every tool. The operators’ precision, i.e., the fraction of the contrasts detected as cracks that actually are cracks, ranges from 73% to 100% and their recall, i.e., the fraction of all actual cracks that have been found, from 30% to 92% for all tools and operators. To achieve the highest precision and recall, we suggest rating the images of an optical near-infrared system combined with luminescence imaging by three operators conjointly for the definition of ground truth for the present as well as for larger sample sets.

Research on Surface Crack Detection Technology Based on Digital Image Processing

Digital image technology is used to monitor the fatigue and fracture of components under the operating conditions of mechanical parts, which is beneficial to monitor the causes of shallow cracks and whether the test results affect the life and safety of parts. The detection technology based on the digital image processing mechanism belongs to the category of non-destructive testing. It can quickly detect the surface cracks of mechanical parts and achieve accurate and effective evaluation of the surface defects of the parts. Based on the application of actual digital image processing technology, this paper presents an effective evaluation system for the defect detection of component surface cracks in image processing technology for programming tools used in simulation experiments, digital image processing algorithms, and design experimental software features, calculation methods and steps. After the above argument, the digital image processing method for surface cracks of parts is proposed. We can properly process the acquired images on the spot, obtain the values of piecewise linear stretching mean, median filtering, etc., and prove that digital image processing technology has the advantages of image detail processing the advantage is that it can realize rapid judgment and accurate detection of surface crack defects.

A Robust Automated Vision Based Filamentous Steel Strip Crack Detection System Based on Neuron Model Segmentation

Crack detection has always been a dominant requirement for steel industries to ensure quality production and seamless infrastructure maintenance. However, application complexities and defect morphological differences make existing approaches confined. Steel-strip surface often undergoes scratch, crack and fatigue conditions during production. Manual crack detection schemes are no longer effective in current day complex environment. Amongst major steel strip crack detection approaches vision based techniques have found potential; Filamentous crack which is caused due to fatigue or strain is fine-grained and thin and hence highly difficult to be detected by classical morphology and static threshold based schemes. In the present work steel strip surface (filamentous) crack detection system has been developed which employs Varying-Morphological Segmentation (VMS) also called Neuron-Model Segmentation (NMS) in conjunction with local directive filtering and active contour propagation. The proposed method can be stated as an augmented variational framework that employs multi-directional filters for local crack-region identification followed by automated multi-directional region growing and iterative contour evolution which performs level set energy minimization to achieve accurate crack detection even under topological non-linearity and varying illumination conditions Simulation results with standard benchmark data has confirmed that the proposed method exhibits satisfactory performance for steel strip surface cracks.

High-temperature all-fiber non-destructive multi-parameter sensing system with consistent performance.

A high-temperature all-fiber non-destructive multi-parameter sensing system is developed. The system can operate consistently in a wide range of temperature changes by specially designed active signal generation and detection units. It is capable of monitoring temperature up to 600°C and cracks on metal pipes with an acoustic wave generation unit and an acoustic detection unit. A gold-coated multi-mode fiber is used to deliver a laser pulse for acoustic excitation while minimizing parasitic acoustic signals at high temperature. An in-fiber Fabry-Perot fiber Bragg grating (FP-FBG) is fabricated in another single-mode fiber and bonded to the test object for acoustic detection. The FP-FBG avoids strain redistribution inside the bonder at high temperature to ensure consistent operation. The feasibility of the system for temperature monitoring and crack detection in real-world applications is also demonstrated on an industry-standard P91 pipe.

Multichannel fiber laser acoustic emission sensor system for crack detection and location in accelerated fatigue testing of aluminum panels

Detection and location of the source of acoustic emission in a thin aluminum panel is demonstrated using a multichannel fiber laser sensor system. Acoustic emission generated by a crack in an aluminum panel used as a test coupon in an accelerated fatigue experiment is detected and the location of the crack identified. Acoustic emission is detected over a bandwidth of around 0.5 MHz from a serially multiplexed array of four laser sensors and compared with measurements taken from four piezo-electric sensors co-located with the fiber laser sensors. The location of the crack is determined by first estimating time difference of arrival of signals at each sensor using a novel algorithm based on the cumulative distribution transform method with hyperbolic positioning. The fiber laser sensor is shown to match the signal-to-noise ratio of the industry standard (Mistras S9225) piezo-electric acoustic emission sensor.

Application of fiber optic light guides with quartz core and cladding and plastic coating as indicators of the emergence of cracks in brickwork

Fiber optic light guides can be used as indicators (witness devices) of the emergence of cracks in masonry composed of construction-grade ceramic bricks. In this study, we experimentally investigated the relationship between the type and size of the fiber optic light guide and the size of the crack opening that caused fiber breakage by physically modeling a crack opening on a special stand. Two sizes of multimode fiber optic light guides with quartz core and cladding and poly(methyl methacrylate) (acrylic) coating were studied in detail. The results are useful for the practical implementation of the proposed method in automatic optoelectronic crack detection systems for brick buildings and structures.

Crack detection system in AWS Cloud using Convolutional neural networks

Abstract In the time on structured surfaces (walls, roofs, bridges, streets, etc.) cracks appear and influence from the aesthetic point of view, but also from the point of view of their resistance and quality. Traditionally, crack detection is performed by human visual inspection, which is dangerous (when they need to climb buildings), subjective (depending on their experience in detecting the severity of a crack), and time-consuming (if we consider hundreds of buildings). Increasingly, artificial intelligence continues to evolve and we can use it to improve human performance and automate the process of crack detection. In order to improve this problem, we present an application that detects cracks in buildings that are difficult to access or would endanger human life. The architecture of our application is based on Convolutional Neural Network. In this paper, three different approaches are described and compared.

Highly Robust and Automated Vision based Filamentous Steel Strip Crack Detection System

Crack detection has always been a dominant requirement for steel industries to ensure quality production and seamless infrastructure maintenance. However, application complexities and defect morphological differences make existing approaches confined. Steel-strip surface often undergoes scratch, crack and fatigue conditions during production. Manual crack detection schemes are no longer effective in current day complex environment. Amongst major steel strip crack detection approaches vision based techniques have found potential; Filamentous crack which is caused due to fatigue or strain is fine-grained and thin and hence highly difficult to be detected by classical morphology and static threshold based schemes. In the present work steel strip surface (filamentous) crack detection system is developed which employs Varying-Morphological Segmentation (VMS) also called Neuron-Model Segmentation (NMS) in conjunction with local directive filtering and active contour propagation. The proposed method can be stated as an augmented vibrational framework that employs multi-directional filters for local crack-region identification followed by automated multi-directional region growing and iterative contour evolution which performs level set energy minimization to achieve accurate crack detection even under topological non-linearity and varying illumination conditions Simulation results with standard benchmark data has confirmed that the proposed method exhibits satisfactory performance for steel strip surface cracks.

Vibration-based plastic-gear crack detection system using a convolutional neural network - Robust evaluation and performance improvement by re-learning

Vibration-based plastic-gear crack detection system using a convolutional neural network - Robust evaluation and performance improvement by re-learning

Railway Route Crack Detection System

This paper proposes a railway route crack detection system. More particularly, this paper presents the detection system that is capable of detecting any cracks or disjoints in the railway tracks by using microcontroller based smart robot. Most of the goods are transferred from one region to another region through railways only due to its low transportation cost, fastness, reliability and less chance of accident. For long distance travel, lots of people prefer railways rather then roadways as trains are very much comfort and fare are also very less. Therefore, for these reasons it is very necessary to provide a safety to the tracks of railways. Hence for this an advance technology-based robot is develop that is capable of locating faults or cracks in the tracks and protect the trains from any accident.