![]() Our analysts strategically guide visionaries to take action now and inspire their business to realize a bigger picture. ABI analysts deliver their conclusions and recommendations in easily and quickly absorbed formats to ensure proper context. ABI’s own research visionaries take stances early on those technologies, publishing groundbreaking studies often years ahead of other technology advisory firms. ![]() This report is part of the company’s AI and Machine Learning research service, which includes research, data, and Executive Foresights.ĪBI Research provides strategic guidance for visionaries needing market foresight on the most compelling transformative technologies, which reshape workforces, identify holes in a market, create new business models and drive new revenue streams. These findings are from ABI Research’s Deep Learning Based Machine Vision in Smart Manufacturing report. Startups that start off as deep learning-based machine vision solution providers are also starting to enable big data processing, process optimization, and yield analytics on their platform,” concluded Su. Deep learning-based machine vision will serve as the right catalyst to move the needle, as the potential is enormous. “Manufacturers are still opening up to adopting AI capabilities into their workflow. ![]() Unlike conventional machine vision which relies on line-by-line coding, deep learning-based machine vision models can be deployed by users without significant coding experience, as these models undergo unsupervised learning based on data gathered. Deep learning-based machine vision requires a robust cloud platform that will enable condition-based monitoring, sensor data collection, and analytics. To implement deep learning-based machine vision technology, manufacturers are encouraged to work with a wide range of vendors, including industrial cloud platform, camera and sensor suppliers, and public cloud vendors. These algorithms can pick up unexpected product abnormalities or defects, providing flexibility and valuable insights to manufacturers. As compared to conventional machine vision which can only detect product defects and quality issues which can be defined by humans, deep learning algorithms deployed for machine vision can go even further. The rich set of data will provide further insight into other aspects of production processes. In addition to cameras, deep learning-based machine vision can also incorporate data collected from various sensors, including LiDAR, radar, ultrasound, and magnetic field sensors. With deep learning-based machine vision, manufacturers can opt to develop their own deep learning-based machine vision systems without the worry of vendor lock-in.” In the past, the choice of machine vision solutions was limited to a handful of companies that performed relatively simple image processing operations. “These AI frameworks can be deployed using on-premise data center infrastructure and a number of software packages from AI companies. The emergence of various open source Artificial Intelligence (AI) frameworks, such as TensorFlow, Caffe2, and MXNet lowers the barrier to entry for the adoption of deep learning-based machine vision,” said Lian Jye Su, a Principal Analyst at ABI Research. “This is in part driven by the democratization of deep learning capabilities. Deep learning-based machine vision, however, is highly flexible due to its ability to be trained and improved using a new set of factory data, enabling manufacturers to incorporate updates and upgrade quickly. Current solutions that are widely deployed in quality control, safety inspection, predictive maintenance, and industrial monitoring rely on preprogrammed rules and criteria, supporting limited ranges of functions. Conventional machine vision is easy to implement but is limited in its capabilities. Manufacturers are on the constant search to upgrade their production yields and workflow efficiency. Deep learning technologies offer so much potential that deep learning-based machine vision techniques in smart manufacturing will see a CGAR of 20% between 20, with a revenue that will reach US$34 billion by 2023, according to ABI Research, a market-foresight advisory firm providing strategic guidance on the most compelling transformative technologies. However, the emergence of deep learning technologies opens the possibility of expanded capabilities and flexibility, leading to more cost efficiency and higher production yield. Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability.
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