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 a variety of manufacturing processes. In the same year, the Asia Pacific area generated 40% of the total revenue and had a high adoption rate.
But how does manufacturing utilize computer vision? 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 Computer Vision Market size was valued at USD 11.22 billion in 2021 and is projected to grow from USD 12.01 billion in 2022 to USD 22.07 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 7% in the forecast period (2023-2030).
The industrial segment of the Computer Vision Market is projected to hold the most lucrative share of more than 50% of the market in 2021. This growth is attributed to the enormous growth in various applications of the computer vision market, particularly in the automotive industry where automation is commonly used for automobile assembly.
The North American region is projected to hold the most substantial share of the market in 2021, driven by favorable government measures aimed at promoting the use of computer vision to ensure quality and ease of operation. The Asia-Pacific region also led the market, accounting for a significant share of worldwide sales, which can be attributed to the considerable increase in computer vision technology investments in Chinese enterprises.
Computer vision technology is being adopted across a range of applications, particularly in the manufacturing industry, where it is being used extensively for automation and robotics. For example, machine vision, a form of computer vision, is used in the quality inspection of manufactured products to detect non-conformities and faults. It’s transforming manufacturing and making industrial operations more autonomous.
URL Reference for images: https://www.skyquestt.com/report/computer-vision-market
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.
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 for manufacturing 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.
The adoption of computer vision solutions for manufacturing involves a series of structured steps. Here’s the roadmap in point format:
In this roadmap, each step plays a crucial role, providing a comprehensive plan for manufacturers to adopt computer vision into their operations.
Computer vision is a technology that enables machines to interpret and understand visual data from the world around them. In the manufacturing industry, computer vision solutions for 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:
Quality control : Employing computer vision technologies allows for automated, continuous inspection throughout the production line. Equipped with machine learning and deep learning capabilities, computer vision systems can identify minute anomalies or defects in products that might escape human detection. For instance, in product labeling or packaging, a computer vision system can check the placement and correctness of labels at high speeds, considerably more accurately than manual inspection. This ensures only top-quality products reach the marketplace, enhancing brand reputation and reducing waste.
Process control : The intricate nature of manufacturing often involves numerous stages, each needing careful monitoring. Computer vision is a powerful tool that can offer real-time monitoring of these processes, helping maintain consistency and alignment with specifications. It can track everything from the correct positioning of components during assembly to the optimal temperature during processing. When it spots any deviations, the system can trigger immediate corrective actions, ensuring high standards and preventing costly errors.
Material handling : Computer vision significantly improves the material handling process within a factory by helping automate tasks like inventory management and warehouse operations. For example, drones equipped with computer vision can monitor stock levels in a warehouse, providing real-time updates and tracking material movement. This not only increases operational efficiency but also reduces manual handling risks, such as accidents or misplaced materials.
Safety : Worker safety in a manufacturing environment is a crucial concern. Computer vision can help enhance safety standards by monitoring the workspace for potential hazards, unsafe behaviors, or protocol violations. Using advanced pattern recognition, it can identify safety breaches, like an operator not wearing safety gear, and can trigger instant alerts to prevent accidents. It can also detect environmental hazards, such as gas leaks or fire, ensuring a safer working environment.
Predictive maintenance : Computer vision solutions for manufacturing can revolutionize maintenance by transitioning from reactive to predictive strategies. By monitoring machinery continuously, computer vision can identify early signs of wear and tear, unusual vibrations, temperature anomalies, or other indicators of potential malfunction. This early detection allows for timely maintenance, reducing unplanned downtime and extending machinery lifespan. In industries like oil and gas, it’s used for detecting cracks in critical components, preventing catastrophic failures.
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.
Computer vision in manufacturing has become an integral part of the industry, improving efficiency, accuracy, and safety. Here’s an example illustrating how each component works:
Image reference: https://www.itransition.com/computer-vision/manufacturing
Lighting module : This component ensures proper and consistent lighting conditions within the manufacturing environment. It enables cameras to capture high-quality images, essential for accurate data analysis and decision-making. Uniform illumination significantly improves the quality of image data, leading to more precise vision-based judgments.
Manufacturing system : This includes all production elements such as assembly lines, robots, and automated guided vehicles. In our example, these components execute the actual assembly and movement of products. They’re designed to carry out specific tasks, their performance monitored by the computer vision system.
Sensing module : The sensing module, with its cameras, acts as the eyes of the system. It collects visual data from the manufacturing system and sends it over IoT to the computer vision system. This data includes information about product movement, assembly procedures, and potentially defective items.
Computer vision system : This is the core of the setup, powered by machine learning algorithms. It analyzes the visual data from the sensing module for various purposes. It can count products, monitor assembly procedures, or even detect anomalies, which could be indicative of defective products.
Decision-making module : Once the computer vision system has performed its analysis, the decision-making module kicks in. This module, equipped with AI algorithms, processes the computer vision system’s results and decides on the best course of action. This decision could range from maintaining current operations to initiating corrective actions.
Actuator : The actuator is the component that physically interacts with the manufacturing system. In response to the decision-making module’s output, the actuator’s robots may change assembly line speed, re-route products, or isolate defective items. This final step completes the loop of the computer vision integrated manufacturing system.
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.
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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