Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional imageprocessingtechniques are useful in solving a specific class of problems. However, these ...
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Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional imageprocessingtechniques are useful in solving a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways of classifying various efforts in literature for surface defect detection using deep learning techniques. These three ways are based on defect detection context, learning techniques, and defect localization and classification method respectively. This article also identifies future research directions based on the trends in the deep learning area.
In the science of agriculture, automation helps to improve the country’s quality, economic growth, and productivity. The fruit and vegetable variety influences both the export market and quality assessment. The marke...
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Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & G...
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Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & Gas industry, its use for inspection activities is even more attractive due to the large structures in these facilities. Therefore, this research proposes deploying an autonomous UAV system to inspect unburied pipelines of onshore O&G facilities. The proposed UAV guiding system is based on imageprocessingtechniques Canny edge detection and Hough Transform to detect the line and on a path follower algorithm to generate the trajectory. The proposed strategy was developed in Robot Operating System (ROS) and tested in a simulated environment considering the practical operational. The same controller was tested on a physical UAV to validate the results obtained in previous simulations. The results demonstrated the effectiveness and feasibility of deploying the proposed strategy for this specific task and the cost reduction potential for real-life operations, as well as reduced potential risks to the physical integrity of the workers.
Underwater imagery is a powerful tool for hydrographic inspection including the bathymetry and aquatic possibilities over the extent of the swath. This paper describes a flexible technique for detecting a specific obj...
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New developments in machine vision and automation technologies provide more sensitive process control and quality inspection in each stage of the production line. Industry 4.0 and imageprocessingtechniques have been...
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Technology has reshaped the workplace and the rapid improvements have transformed how we work nowadays. In the pursuit of industry 4.0, we build smart machines and robots to replace manual labor. While the manual labo...
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Technology has reshaped the workplace and the rapid improvements have transformed how we work nowadays. In the pursuit of industry 4.0, we build smart machines and robots to replace manual labor. While the manual labor is replaced by machines, in many cases, humans are transformed into desktop software users. Jobs such as testing, quality inspection, data monitoring, data entry, and routine editing remain to be done by humans in front of desktop computers. The operations to software applications in principle can be reduced to screen output understanding and mouse and keyboard operations. When the characteristics of these jobs are repetitive, tedious, and monotonous, they can be replaced by GUI automationtechniques. GUI automation can be achieved by different underlying technologies, each has its pros and cons. In this paper, we describe a tool-Korat, which uses computer-vision to achieve maximum cross-platform capability for industrial applications, including test automation and robotic process automation. Although Korat has been successfully adopted by several industrial customers, difficult problems remain to be addressed. The problems and difficulties in applying computer vision for GUI automation are discussed and studied in this paper, particularly the experiences of applying open source OCR to GUI automation over color screenshots. By introducing critical pre-processing stages and algorithms, the recognition rate is significantly increased and becomes feasible for practical usage.
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