RAILWAY AGE, JULY 2021 ISSUE: Reliable, real-time monitoring of in-service railcar components will enhance the potential for maintenance planning.

BY ANISH POUDEL, PH.D – PRINCIPAL INVESTIGATOR I (NDT); ABE MEDDAH – PRINCIPAL INVESTIGATOR I; AND MATT WITTE, PH.D – SCIENTIST, TRANSPORTATION TECHNOLOGY CENTER, INC. 

Through the Association of American Railroads (AAR) Strategic Research Initiatives (SRI) program, Transportation Technology Center, Inc. (TTCI) has been assisting suppliers and other stakeholders in the development of machine vision technologies and related algorithms for evaluating railcar components and conditions. To enhance safety and reduce worker exposure to yard risk, North American railroads have begun to install machine vision inspection systems in revenue service. 

Using commercially available deep learning system platforms, TTCI researchers developed and demonstrated three convolutional neural network-based applications for analyzing visual images. A convolutional neural network is a type of artificial neural network that uses machine learning algorithms to analyze digital images. Convolutional neural networks are more powerful and effective than traditional artificial neural networks at recognizing, interpreting, and categorizing large, unstructured data sets; particularly those comprised of visual imagery. The convolutional neural networks that TTCI demonstrated were used to identify and evaluate components on the railcar truck. Specifically, convolutional neural networks were developed to identify truck type, detect the location of a spring group and measure the compression of spring coils to determine load conditions.

A convolutional neural network must be trained to recognize and categorize data. This training is accomplished “by example” and requires two components: 1) A sampling of images representative of the type expected to be processed by the convolutional neural network, and 2) A specified desired output for when the network recognizes—or does not recognize—certain objects in each of these images. Grayscale JPEG images with 2048 × 2048 resolution were deemed adequate for developing and testing the applications. These images were obtained from TTCI’s manual viewer database system, which contains machine vision data from vision systems on site at the Federal Railroad Administration Transportation Technology Center (TTC), as well as images from a revenue service location (Stewart, Monique, Matthew Witte, and Abe Meddah. August 2020. “In-Service Performance of a Truck Component Inspection System,” Federal Railroad Administration, Office of Research and Development, DOT/FRA/ORD-20/31, Washington, D.C.). A total of 1,276 images were used to build and test these applications and to evaluate the overall possibility of machine learning technology.

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Will computer vision take human jobs in the upcoming years with cutting-edge technologies?

There is a worldwide controversy whether computer vision will take over human jobs that can increase the rate of unemployment. The integration of cutting-edge technologies with AI algorithms into the existing computer system has introduced advanced computer vision. AI algorithms transform multiple sets of real-time data into appropriate business insights without any human intervention. But AI algorithms and computers require human assistance to complete multiple tasks efficiently and effectively. Have you ever wondered why there is an increase in human jobs in the field of AI in these recent years? Human skills are needed for the advancement in software development as well as innovating new technologies to boost productivity. Reputed companies and startups provide job opportunities such as computer vision engineer, computer vision scientist, deep learning specialist, software developer, data scientist, software engineer, lead scientist, data analytics lead, computer vision research engineer, and many more with lucrative salary package. That being said, we can claim that computer vision will not take human jobs but will ease the workload effectively to achieve higher ROI.

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How does computer vision ease the workload in human jobs?

Computers analyze multiple sets of raw data from digital images and videos to provide appropriate decisions by understanding the environment through the new form of AI known as computer vision. The data is more accessible and affordable due to digital transformation and globalization. Computer vision is thriving in recent years because of the accuracy rates for image pattern recognition than humans. Deep learning neural networks enable the iterative learning process in computers to acquire, process, and analyze image patterns efficiently and effectively than the human visual cognitive system. Convolutional Neural Network (CNN) is used in computer vision technology for appropriate image pattern recognition. These neural networks scan the available image pixel by pixel to identify patterns and memorize the ideal output from different characteristics such as contours and colors. Humans are needed to develop smart machines for completing automated tasks with visual cognition.

The computer vision system can be used for object classification, object identification, and object tracking. It takes much lesser time to analyze thousands of images as well as to detect any defect or issue with hi-tech cameras, data, and AI algorithms than the naked human eyes. Let’s explore the potential of computer vision across several industries to boost productivity.

• Automotive: Computer vision through ADAS, RADAR as well as LIDAR technologies provide visual representations, high visibility, and 3D representations of the surroundings respectively. Automotive Gesture Recognition also monitors the facial and hand gestures of drivers with audible and visual alerts.
• Retail: Computer vision helps in security through CCTVs, spillage detection, theft control, video analytics, enhancing the shopping experience, optimizing operations, alerting in-shelf productivity, and better customer engagement
• Manufacturing: Computer vision helps factory workers in predictive maintenance, identifying defects and eliminating risks as well as product quality inspection for minimal waste of products
• Healthcare: Computer vision can detect any unusual image pattern in reports and X-rays accurately, early-stage tumors, arteriosclerosis, and many more for doctors and nurses to operate at the right time
• Agriculture: Computer vision detects pests and plant diseases, information about high-quality crops, provides facial recognition to identify the individual animal, and analyses grain quality for farmers efficiently

Thus, we all can confirm that computer vision is not taking away human jobs but there is a high probability that it will generate more human job opportunities in the upcoming years. The aim of computer vision is to collaborate with humans to enhance the workload with more appropriate outcomes without any failure or mistake. We have to remember that humans are the main creator behind all these achievements of computer vision.

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Artificial intelligence applications support doctors’ diagnostic decisions, automate certain tasks, says Turkish social media expert

ANKARA

A Turkish social media expert said artificial intelligence (AI) brings a new vision to the healthcare sector.

“Artificial intelligence brings revolutionary developments in the field of health, as in all areas of life,” Deniz Unay told Anadolu Agency.

“Machine learning and assisted artificial intelligence have features that can develop an entire health system within the framework of a new vision,” Unay said.

Unay said the AI applications support doctors’ diagnostic decisions and automate certain tasks underlining AI’s rise in healthcare applications.

AI can help better and more accurate detection of symptoms, analyzing the side effects of treatments, and processing large amounts of data produced by healthcare facilities, he said.

However, AI in the medical field is an area that is not yet fully sufficient and is considered to be developed especially for automated robotic surgery applications, he warned.

“Artificial intelligence software and robots, which will assist doctors in many areas, enable faster and safer health services,” he said, adding that AI developments in medicine are planned to be used widely on both general health practices and drugs.

Everyone in the field of health, from specialist doctors to first-aid workers, will start to benefit from artificial intelligence technology soon, he added.

The revenue from AI systems in healthcare worldwide in 2021 exceeded $6.6 billion, according to data from Germany-based statistics company Statista.

The investment amount is expected to increase significantly in line with the increasing desire to use artificial intelligence and robots in the health sector, he said, adding that the increase will lead to easier use of health services by large groups.

“Great importance is given to the use of artificial intelligence in the health sector to spread health services to wider areas. Health services offered in various regions are expected to increase, especially with the technological infrastructure,” he continued.

With the spread of medical instruments, doctors are forced to consider more data, he added.

He went on to say that AI is mostly used in medical imaging and interpretation of radiology.

Underlining that some cancers, such as lung or breast cancer, are tough to identify in images produced by scanners, he said programs can identify abnormalities that cannot be detected with the naked eye to detect early tumors more reliably and target better treatments.

Until recently, AI in healthcare was limited to research or predictive analytics, he said. Much focus is now on developing technologies that can improve robot-assisted surgery, he added.

“There are already significant uses of artificial intelligence that prove how it can improve the techniques used for several years in the field of robotic surgery, especially in the field of microsurgery,” Unay noted.

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• Executes projects in the field of advanced automated horticultural production systems with a focus on computer vision and machine learning applications; contributes to the development of sustainable (indoor) horticultural crop production systems.
• Develops new image analysis and control software for the detection of crop growth parameters of various crops, the quality detection of fresh products and crops and the detection of plant health and resilience; uses various camera techniques such as RGB, 3D, hyperspectral or thermal imaging.
• Applies modern machine learning methods such as deep learning and reinforcement learning, develops new AI algorithms for growing horticultural crops with a minimum of resources; deals with time series; uses various sensors; deals with data fusion.
• Leads and works in projects together with other academic and industrial partners.
• Conducts applied scientific research within the projects.
• Communicates relevant research results to science and practise.

Requirements:

We are looking for an enthusiastic and ambitious researcher computer vision and machine learning, with:

• Academic working and thinking on the field of computer vision and machine learning, study in e.g. artificial intelligence, natural or biomedical science, holds a PhD.
• Knowledge of various image analysis techniques, data analysis methods, classification and statistics.
• Knowledge of various modern machine learning techniques, such as deep learning and reinforcement learning methods.
• Experience with the structured development of complex software, programming in C++ and C#; experience with Halcon and / or LabView and / or Matlab is an advantage.
• Knowledge of lighting, optics and camera systems (2D, 3D) including calibration and control is an advantage; knowledge of various sensor types, data output and practical usage of such sensors is an advantage.
• Experience with the application of image processing and machine learning in the green environment and willingness to learn the challenges of horticulture.
• Willingness to combine theoretical knowledge with practical setups and implementation.
• Experience with project-based work and project leadership.
• Excellent knowledge of Dutch and English language.
• Driving License B.
• Is an enthusiastic independent researcher with good communication skills and team spirit.
• Is customer- and quality-oriented and stress tolerant.

Salary Benefits:

A challenging position with, depending on your experience, a competitive salary up to a maximum of € 4.934,- gross per month for a full working week of 36 hours in accordance with the Collective Labor Agreement for Wageningen Research (scale 11). At WUR you work in a (inter-)national leading organization in the field of research and education. This is a position for a duration of one year with the prospect of permanent employment if our cooperation is mutually satisfactory.

In addition, we offer:

• 8% holiday allowance;
• a fixed end-of-year bonus of 3%;
• excellent training opportunities and secondary employment conditions;
• flexible working hours and vacations can be determined in consultation with colleagues in such a way that an optimal balance between work and private life is possible;
• excellent pension plan through ABP;
• 171 vacation hours, the possibility to purchase extra and good supplementary leave schemes;
• a choice model to put together part of your employment conditions yourself, such as a bicycle plan;
• make use of the sports facilities on campus for a small fee.

Wageningen University & Research stimulates internal career opportunities and mobility with an active internal recruitment policy. There are ample opportunities for own initiative in a learning environment. We offer a versatile job in an international environment with varied activities in a pleasant and open working atmosphere.

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