Crack detection in concrete tunnels using a gabor filter invariant to rotation

Producción CientíficaIn this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.Ministerio de Economía y Competitividad under project Ref. IPT-2012-0980-370000Ministerio de Ciencia e Innovación, research project Ref. DPI2014-56500Junta de Castilla y León Ref. VA036U14

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Department of Mehcanical EngineeringUnmanned aerial vehicles (UAVs) are widely used in various areas such as exploration, transportation and rescue activity due to light weight, low cost, high mobility and intelligence. This intelligent system consists of highly integrated and embedded systems along with a microprocessor to perform specific task by computing algorithm or processing data. In particular, image processing is one of main core technologies to handle important tasks such as target tracking, positioning, visual servoing using visual system. However, it often requires heavy amount of computation burden and an additional micro PC controller with a flight computer should be additionally used to process image data. However, performance of the controller is not so good enough due to limited power, size, and weight. Therefore, efficient image processing techniques are needed considering computing load and hardware resources for real time operation on embedded systems. The objective of the thesis research is to develop an efficient image processing framework on embedded systems utilizing neural network and various optimized computation techniques to satisfy both efficient computing speed versus resource usage and accuracy. Image processing techniques has been proposed and tested for management computing resources and operating high performance missions in embedded systems. Graphic processing units (GPUs) available in the market can be used for parallel computing to accelerate computing speed. Multiple cores within central processing units (CPUs) are used like multi-threading during data uploading and downloading between the CPU and the GPU. In order to minimize computing load, several methods have been proposed. The first method is visualization of convolutional neural network (CNN) that can perform both localization and detection simultaneously. The second is region proposal for input area of CNN through simple image processing, which helps algorithm to avoid full frame processing. Finally, surplus computing resources can be saved by control the transient performance such as the FPS limitation. These optimization methods have been experimentally applied to a ground vehicle and quadrotor UAVs and verified that the developed methods offer an optimization to process in embedded environment by saving CPU and memory resources. In addition, they can support to perform various tasks such as object detection and path planning, obstacle avoidance. Through optimization and algorithms, they reveal a number of improvements for the embedded system compared to the existing. Considering the characteristics of the system to transplant the various useful algorithms to the embedded system, the method developed in the research can be further applied to various practical applications.ope

Multi-image-feature-based hierarchical concrete crack identification framework using optimized SVM multi-classifiers and D-S fusion algorithm for bridge structures

Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%

Railway freight transport and logistics: Methods for relief, algorithms for verification and proposals for the adjustment of tunnel inner surfaces

In Europe, the attention to efficiency and safety of international railway freight transport has grown in recent years and this has drawn attention to the importance of verifying the clearance between vehicle and lining, mostly when different and variable rolling stock types are expected. This work consists of defining an innovative methodology, with the objective of surveying the tunnel structures, verifying the clearance conditions, and designing a retrofitting work if necessary. The method provides for the use of laser scanner, thermocameras, and ground penetrating radar to survey the geometrical and structural conditions of the tunnel; an algorithm written by the authors permits to verify the clearances. Two different types of works are possible if the inner tunnel surfaces interfere with the profile of the rolling stock passing through: modification of the railroad track or modification of the tunnel intrados by mean milling of its lining. The presented case study demonstrates that the proposed methodology is useful for verifying compatibility between the design vehicle gauge and the existing tunnel intrados, and to investigate the chance to admit rolling stocks from different states. Consequently, the results give the railway management body a chance to perform appropriate measurements in those cases where the minimum clearance requirements are not achieved

Performance Comparison of Hybrid CNN-SVM and CNN-XGBoost models in Concrete Crack Detection

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the intent of this study is to address the above problem using two-hybrid machine learning models and classify the concrete digital images into having cracks or non-cracks. The Convolutional Neural Network is used in this study to extract features from concrete pictures and use the extracted features as inputs for other machine learning models, namely Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost). The proposed method is evaluated on a collection of 40000 real concrete images, and the experimental results show that application of XGBoost classifier to CNN extracted image features include an advantage over SVM approach in accuracy and achieve a relatively better performance than a few existing methods

Automatic Road Crack Segmentation Using Thresholding Methods

Maintenance of good condition of roads are very essential to the economy and everyday life of people in a every country. Road cracks are one of the important indicators that show degradations of road surfaces. Inspection of roads that have been done manually took a very long time and tedious. Hence, an automatic road crack segmentation using thresholding methods have been proposed in this study. In this study, ten road crack images have been pre-processed as an initial step. Then, normalization techniques, L1-Sqrt norm have been applied onto images to reduce the variation of intensities that skewed to the right. Then, thresholding methods, Otsu and Sauvola methods have been used to binarize the images.  From the experiment of ten road crack images that have been done, normalization technique, L1-Sqrt norm can help to increase performance of road crack segmentation for Otsu and Sauvola methods. The results also show that Sauvola method outperform Otsu method in detecting road cracks

Multi-image-feature-based hierarchical concrete crack identification framework using optimized svm multi-classifiers and d–s fusion algorithm for bridge structures

Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%

What's cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification

Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided

DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGES

DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGE

Deep Learning for Crack-Like Object Detection

Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention