Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices

Detection of Road Surface Damage Using Mobile Robot Equipped with 2D Laser Scanner

Abstract-This paper introduces a road surface damage detection using mobile robot. Our research is aimed autonomous sidewalk investigation with mobile robot, for reduce the burden of human workers engaged in road maintenance. A mobile robot moves along the route for investigation and obtain shape information of road surface using 2D laser scanner. From this road surface information, road damage section will be automatically detected. By showing the detection result instead of site investigation by human workers, it expects to reduce the burden of human workers. Road surface have gradual curves and some road damage is small and less than 2 cm. Hence, our method uses random sampling to detect irregularity as road damage. This paper explains the measurement of road surface using mobile robot equipped with 2D laser scanner and the road damage detection method. In this paper, some experimental results also is shown

Study on Energy Accumulation and Dissipation Associated with Coal Burst

Coal burst, which refers to the brittle failure of coal, has been a serious hazard for underground coal mining, particularly at greater depth. Massive energy accumulated in coal could be dissipated almost instantaneously in the form of kinetic energy when the loading stress exceeding the ultimate strength of coal. This thesis qualitatively and quantitatively examines the energy accumulation and dissipation process associated with coal burst through a comprehensive research program of literature review, theoretical analysis and experimental studies. The energy accumulation sources, dissipation forms and its influencing factors of coal burst are reviewed based on the energy conservation law and the static-dynamic loads superposition theory. The burst energy is provided by static loads including gravitational and abutment stress, and dynamic loads including fault slipping and roof weighting. Studies indicated that the main driving energy source of coal burst occurred in Australian coal mines resulted from elastic energy storage that has been accumulated during the loading process of coal

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Nondestructive Testing in Composite Materials

In this era of technological progress and given the need for welfare and safety, everything that is manufactured and maintained must comply with such needs. We would all like to live in a safe house that will not collapse on us. We would all like to walk on a safe road and never see a chasm open in front of us. We would all like to cross a bridge and reach the other side safely. We all would like to feel safe and secure when taking a plane, ship, train, or using any equipment. All this may be possible with the adoption of adequate manufacturing processes, with non-destructive inspection of final parts and monitoring during the in-service life of components. Above all, maintenance should be imperative. This requires effective non-destructive testing techniques and procedures. This Special Issue is a collection of some of the latest research in these areas, aiming to highlight new ideas and ways to deal with challenging issues worldwide. Different types of materials and structures are considered, different non-destructive testing techniques are employed with new approaches for data treatment proposed as well as numerical simulations. This can serve as food for thought for the community involved in the inspection of materials and structures as well as condition monitoring

A New Vehicle Image Dynamic Tracking Approach

Abstract: For the purpose of moving target’s detecting, a new image tracking approach based on the changing factor of reference background learning and the direction predicting operator is presented. The high-speed moving vehicle is used as the detecting target for establishing a changing factor, on this basis the image sequence’s reference background learning is conducted, then by cross-correlation matching and coordinate transforming the moving target-vehicle is coordinate-positioned and speed-determined, combining with the direction predicting operator the moving direction of the target vehicle is predicted, thus the vehicle imagetracking can be realized. Through tracking experiment and performance comparison it is proved that by this new approach an accurate and stable tracking results of moving vehicle can be gotten; a new idea can also be provided for the research of moving target’s image-monitoring. Copyright © 2014 IFSA Publishing, S. L

Novel methods of object recognition and fault detection applied to non-destructive testing of rail’s surface during production

A series of rail image inspection algorithms have been developed for Tata Steels Scunthorpe rail production line. The following thesis describes the contributions made by the author in the design and application of these algorithms. A fully automated rail inspection system that has never been implemented before in any such company or setup has been developed. An industrial computer vision system (JLI) already exists for the image acquisition of rails during production at a rail manufacturing plant in Scunthorpe. An automated inspection system using the same JLI vision system has been developed for the detection of rail‟s surface defects during manufacturing process. This is to complement the human factor by developing a fully automated image processing based system to recognize the faults with an improved efficiency and to allow an exhaustive detection on the entire rail in production. A set of bespoke algorithms has been developed from a plethora of available image processing techniques to extract and identify components in an image of rail in order to detect abnormalities. This has been achieved through offline processing of the rail images using the blended use of different object recognition and image processing techniques, in particular, variation of standard image processing techniques. Several edge detection methods as well as adapted well known Artificial Neural Network and Principal Component Analysis techniques for fault detection on rail have been developed. A combination of customised existing image algorithms and newly developed algorithms have been put together to perform the efficient defect detection. The developed system is fast, reliable and efficient for detection of unique artefacts occurring on the rail surface during production followed by fault classification on the rail imaging system. Extensive testing shows that the defect detection techniques developed for automated rail inspection is capable of detecting more than 90% of the defects present in the available data set of rail images, which has more than 100,000 images under investigation. This demonstrates the efficiency and accuracy of the algorithms developed in this work

APPLICATIONS OF INNOVATIVE BUILDING MATERIAL AND COMPUTER VISION METHODS IN GEOTECHNICAL ENGINEERING

Ph.D