MAY 2020CIOAPPLICATIONS.COM9The future of Machine Vision lies in enhancement of processing speed integrated with improved modeling capabilitiesAlgorithmic DevelopmentsVisual understanding is difficult since it requires knowledge beyond the objects present in image; it represents the ability to comprehend actions and goals of the subjects in an image of video. This is very straightforward for humans but immensely complex for an algorithm. In 2012,Alexnet achieved state of the art performance in image recognition capability against all machine learning and traditional computer vision techniques and this led to a cambrain explosion in Machine Vision. Current deep learning models perform better than humans when provided with enough examplesThese achievements have been possible because of well-timed hardware and algorithmic advancements, and the availability of examples or, what is more commonly known asannotated data in machine learning. Machine Vision ApplicationsTo understand the utility of Machine Vision let us deep dive into a few industries that are getting transformed with computer vision.· Automobile Industry: Every year, traffic accidents account for more 2% of deaths and alarge percentage of people also get severely injured due to human error. Currently, machine vision techniques are being used to incorporate safety features that makes sure the driver stays within the lane and are not too close to other cars. There are also features that help with tight parking situations. Autonomous vehicles are expected to use cameras and sensors all around the car and perform multiple tasks simultaneously to operate the vehicle without human intervention. · Healthcare: Machine vision is being used in healthcare in a number of applications ranging from predicting heartbeat rhythm disorders to tracking blood loss during childbirth. The advantages of machine vision techniques are precision and timely detection of rare diseases. Precision medicine can prevent unnecessary invasive surgeries or expensive medications. Machines are able to attain precision from several examples that humans might miss due to sensory limitations. Timely detection of fatal diseases such as cancer with pattern recognition employed by machine vision techniques will have a large impact on saving lives. Machine Vision can also provide assistance to doctors by taking over tedious tasks and making them more efficient. The ability to make predictions at an individual level will make personalized medicine achievable.Machine vision is also fundamentally changing industries such as quality assurance: defects in production line are automatically detected. For retail, customers could potentially walk into a store and pick the item they want and walk out and there will be a seamless transaction performed by an automated system using machine vision. Other industries that are experiencing a transformation are robotics, agriculture, fitness, education, finance, marketing to mention a few.What does this mean for your business?The possibilities of machine vision solutions to improve a business can be numerous. It is important to identify the problems that can be solved by machine vision and get stakeholder buy in. The next step is to decide whether to build a solution in-house or to buy services. If the problem can be cast into a standard problem, such as facial recognition or object detection, then buying services is a good place to start. If the problem is custom or domain specific, building solutions makes more sense. This requires expertise which can be achieved through in-house training and external hires. Another important part of achieving an accurate model is having examples or annotated data that deep learning models need, to learn patterns. It is important to collect good-quality annotated data by identifying reliable third-party data vendors with track record of reliable subject matter experts. The secret to successfully implement emerging technologies, such as machine vision to add value to a company, lies in its ability to invest in all these components. Research and Development is another important part of success in machine vision since the field is rapidly evolving.The future of Machine Vision lies in enhancement of processing speed integrated with improved modeling capabilities. An important factor in adapting vision technology lies in ease of use by the end user who could be a factory operator or a medical staff. In the future, building machine vision capabilities will also get easier with availability of higher-level abstractions of algorithms and accessible solutions.
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