Keyur B
January 18, 2023
12 minutes to read
Modern technology is being adopted by the manufacturing sector to streamline operations and boost output. The industrial sector is today influenced by artificial intelligence, cloud computing, machine learning, the Internet of Things (IoT), Industry 4.0, and computer vision.
Every step of the production process, from obtaining raw materials to providing and distributing the completed goods, uses computer vision. It is essential for giving the manufacturing unit flexibility and scalability. It aids in boosting output while maintaining quality and utilizing resources sparingly. Manufacturers work with offshore solution providers and AI developers to modernize their infrastructure through digital transformation.
A branch of artificial intelligence and computer science called computer vision enables machines to read, comprehend, and evaluate visual input just like people do. AI makes machines think, but computer vision enables them to perceive and comprehend pixel-level visual data. Computer vision can be used to read and process images, videos, and other visual inputs.
By 2028, the global computer vision market, which was valued at approx. $15 billion in 2022, is anticipated to expand at a CAGR of 7.3%. About 51% of market revenue was attributable to the industrial sector. More than half of the overall global share was contributed by computer vision in manufacturing industry processes. In the same year, the Asia Pacific area generated 40% of the total revenue and had a high adoption rate.
But how does utilization of computer vision in manufacturing industry work? How do manufacturers aim to alter their systems and procedures as more and more firms seek to incorporate computer vision and Industry 4.0? Let’s look at it.
In order to elicit the appropriate responses and help humans with a variety of production-related tasks, computer vision in manufacturing focuses on developing artificial systems that can capture, process, and thus understand visual inputs from the physical world (primarily factories and other industrial sites).
The simplest forms of computer vision, used in manufacturing as well as other industries, can recognize specific objects and prompt a response using a rule-based principle. Specifically, they do this by identifying specific features in the captured visuals and determining whether they match a set of predetermined parameters. This method is less effective at handling the finer distinctions and variances that frequently appear when working with unstructured data sources like images or videos and is prone to producing a lot of false positives.
These problems have been addressed by the most recent developments in AI, machine learning (ML), and deep neural networks, which have enabled manufacturing companies to improve their computer vision systems with self-improving algorithms that can recognize recurrent visual patterns and relate them to specific items through experience. Practically speaking, ML-powered computer vision solutions can be trained with millions of images to autonomously spot the typical features of each object, learn to recognize them, and even fine-tune their performance over time (including products and machinery with very complex structures or in anomalous conditions).
As a result, there are now more manufacturing-related applications than ever before, with improved precision, context awareness, adaptability, and reactivity to novel visual features.
The COVID-19 outbreak’s operational and logistical interruption over the past two years has put pressure on a manufacturing sector already suffering from a ten-year productivity decline. The deployment of new cutting-edge technologies and the ensuing shift to the Industry 4.0 model have been greatly accelerated by the necessity to find ways to regain momentum and make this industry more resilient in highly unstable conditions. One of the key features of this transformation is undoubtedly the increasing use of computer vision in industrial operations, which makes the manufacturing sector stronger in the following ways:
The use of computer vision-powered robots and other automation systems that operate around-the-clock speeds up manufacturing cycles, resulting in a 12% increase in labour productivity and a 10% increase in overall production output.
Automation and computer vision-based maintenance can increase productivity while decreasing machine downtime, which results in lower operating costs overall.
Robots powered by computer vision operate with surgical precision, guaranteeing greater product quality and a general 10–20% decrease in the cost of QA processes.
The use of computer vision in manufacturing can also be used to spot issues that could jeopardize the workers in the plant, as well as to monitor employee conditions and recognize indicators of fatigue or discomfort.
The use of computer vision technology can greatly assist in overcoming various operational challenges in the manufacturing industry and improving overall efficiency. By leveraging the power of computer vision, manufacturers can automate processes, reduce errors, and increase productivity, ultimately leading to cost savings and enhanced competitiveness in the market.
Let’s define some broad criteria that could be useful for resolving the difficulties of computer vision deployment in a manufacturing environment and better outline the aforementioned issues:
Data and a supporting technological infrastructure are required for AI-powered computer vision because datasets generally acquired for routine process monitoring may not be appropriate for machine learning algorithms and may even be detrimental. Network and system upgrades at the manufacturing facility will need to be properly funded and executed.
In terms of investments, top management and other interested parties might be hesitant to allocate a sizable amount of the budget to computer vision and associated technologies. Setting up a progressive implementation plan that guarantees modest, short-term outcomes is a feasible solution. Such accomplishments ought to persuade management and investors of the potential benefits of digitization.
The employees need to be persuaded as well, as any technology that promotes automation could have a negative impact on their employment. In this regard, think about the potential for upskilling your personnel through specific training courses to improve their interaction with computer vision tools and employing new specialized professionals with strong tech skills to work alongside them.
New technology could not seamlessly integrate with current business procedures. Once again, we recommend introducing computer vision gradually to allow workers to become accustomed to the technology while also correctly readjusting the manufacturing workflow. Establishing centres of excellence to supervise the deployment of these solutions is another need.
The initial obstacle, choosing a use case, is one that many businesses never even get past. The golden rule in this situation is to follow the money, which means picking an application area that may benefit most from computer vision by, for instance, choosing bottleneck-prone tasks like quality control or product tracing.
Computer vision is a technology that enables machines to interpret and understand visual data from the world around them. Computer vision applications in manufacturing can be used in a number of ways to improve efficiency, reduce costs, and increase quality. Some potential use cases of computer vision in manufacturing include:
Overall, computer vision has the potential to greatly improve the efficiency and effectiveness of the manufacturing industry, and is likely to play an increasingly important role in the industry in the future.
Augmented reality (AR) technology, when combined with computer vision, can greatly enhance productivity in the manufacturing industry. Some specific use cases for computer vision in AR for manufacturing include real-time quality control and inspection, training and onboarding of new employees, and remote assistance for maintenance and repair tasks.
The AR-powered products by Plutomen do an exceptional job at letting the remote experts get the vision of the field workers. It provides a multitude of use cases across industries.
An AR-powered remote assistance & video collaboration platform that enables your frontline workforce to connect with your experts instantly through computer vision. Through Connect, you can deliver quick incident response, and expedite your field servicing process with one-click connect. Enterprises can minimize downtime, improve first-time fix rates and conduct field tasks – MRO, Troubleshooting, Compliance & Audits checks remotely.
An AR-powered work assistance platform for accessing digital work instructions that streamlines workforce knowledge with the help of computer vision in manufacturing. The product digitalizes compliance audits and inspections, from execution & submission to data management. It comprises task checklists, work SOPs, and lets you design effective audits & inspections modules.
An AR-powered self-assistance platform for next-gen workforce training, digitalization of the process, and providing an interactive knowledge repository. Usage of Assist expedites performance of the onboarded workforce and upskill them with everchanging trends. Reduce the training time and boost new hire productivity through an on-click and on-floor immersive training experience.
Consider using computer vision in manufacturing surrounding, which provides the resources required by your staff remotely. Plutomen specializes in the AR solutions that can satisfy your unique requirements based on the industry.
Computer vision has had a significant impact on the manufacturing industry, revolutionizing the way in which products are made and processes are managed. Some specific ways in which computer vision has impacted the manufacturing industry include:
Overall, the use of computer vision in the manufacturing industry has the potential to greatly benefit the industry and drive progress.
One of the biggest challenges of implementing computer vision in manufacturing is the initial cost and resources required to set up the system. Computer vision systems often require specialized hardware, such as cameras and sensors, as well as software and algorithms to process and analyze the data. Additionally, implementing computer vision may require changes to existing infrastructure and processes, which can also be costly.
Another challenge is the need for trained personnel to operate and maintain the system. While computer vision has the potential to automate many tasks and reduce the need for manual labor, it still requires skilled workers to set up and maintain the system.
Finally, there may be challenges related to data privacy and security when implementing computer vision in manufacturing. It is important for companies to properly secure and protect the data collected by the system to ensure compliance with relevant regulations and protect sensitive information.
Overall, while the benefits of implementing computer vision in manufacturing can be significant, it is important for companies to carefully consider the costs and challenges involved in order to successfully implement the technology.
There are several current trends in computer vision for industrial automation:
Overall, these trends are leading to the development of more sophisticated and efficient computer vision systems for industrial automation, which can greatly benefit the manufacturing industry.
With more than 12+ years of experience in the world of enterprises, technology, and metaverse, Keyur Bhalavat is leading Plutomen to gain meaningful partnerships & to have a strong clientele network. He is one of the board members of GESIA (Gujarat IT Association Ahmedabad).
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