The growing demand for technological advancement and optimization of production cycles drives manufacturing corporations to adopt advanced systems. One of the pioneers of this expansion is computer vision, which facilitates growth and improvement in various areas and supplements operational processes across numerous industries.
Computer vision in manufacturing leverages smart video and image monitoring, recognizing complex patterns and identifying objects the way humans do. This ability enables it to handle repetitive tasks, catch product defects, pinpoint production inefficiencies, and much more.
Our guide, created by experts at Tensorway, will shed light on this topic and give you a comprehensive understanding of how this technology is revolutionizing the manufacturing market. We will also explore the various use cases of computer vision in this sector, as well as the benefits and challenges associated with such systems. Let’s get started.
Market Overview of Computer Vision in Manufacturing
Computer Vision (CV) derives from artificial intelligence, allowing machines to essentially “see” images and videos. Typically, visual devices, such as cameras, are used for CV to capture and process information, with the ultimate goal of providing useful insights.
Unlike human eyesight, computer vision relies on trained algorithms to understand the context behind the imagery. This is usually propelled by deep learning, specifically convolutional neural networks (CNNs) that analyze raw data to recognize patterns.
Computer vision in manufacturing industry is used to monitor production lines and workplaces, but we’ll get to its use cases later. For now, to help you grasp how CV works, we will break it down into three digestible techniques:
- Object detection. The system detects specific objects within an image or video feed, such as a production component, and classifies them based on their shape, boundaries, colors, etc.
- Object recognition. According to the features of the object, CV matches it within its dataset and recognizes it as a particular thing, like a vehicle engine.
- Segmentation. The process aims to categorize the object into a certain class by breaking down the image into pixels. It’s what allows computer vision to separate objects it sees into distinct groups, even if they belong to the same class.
This should give you a basic idea of why CV technology found demand in manufacturing. In fact, the scope of implementation is substantial, with the computer vision market size being valued at $20.31 billion in 2023 and projected to reach $175.72 billion by 2032. It indicates a compound annual growth rate (CAGR) of 27.3% over the period.
By far, manufacturing takes the cake and holds the largest market share of CV adoption. The image recognition tech is what drives this demand in the production sector, as such systems have the potential to largely boost automation and efficiency.
The main appeal of computer vision in manufacturing lies in its ability to control the quality and detect defects robustly, accelerate assembly processes, and identify early signs of equipment failure, all of which can reduce operational costs. This diverse application of CV attracts corporations from all kinds of manufacturing sectors: automotive, electronics, retail, energy, pharmaceutical, and so much more.
As the advancements of artificial intelligence rise, production businesses look for ways to integrate cutting-edge technology into their operations. Not only does this enhance quality and minimize costs, but it also gives companies a competitive edge by accelerating production lines and supply chains without compromising customer satisfaction.
While no one has the ability to glimpse into the future, studies indicate that the adoption of computer vision for manufacturing will only continue to grow. As more organizations consider integrating this tech into their work, CV has the potential to become a mainstream approach in the coming years.
After all, innovation drives progress, and those who fail to meet emerging standards will be left to bite the dust. Which is why it’s time to learn the many applications of computer vision in the production industry.
9 Applications of Computer Vision in Manufacturing with Examples
Computer vision has a wide range of use cases across a multiplicity of industries. In manufacturing specifically, CV can be applied to automate several critical processes, oversee production, monitor for defects, derive crucial insights in maintenance, and much more.
With computer vision development, corporations can significantly streamline operational workflow and improve conditions where traditional systems or human intervention are typically insufficient.
Let’s take a deep dive into nine popular applications of CV in manufacturing. To broaden your perspective on how you can use this technology, we will also provide you with real-life examples from renowned organizations.
Product Design and Engineering
One of the most commonly known computer vision applications in manufacturing lies in product design. Creative specialists can generate 3D object designs to streamline prototyping by visualizing them in real-world environments. Augmented Reality (AR) technology can further help with product creation by allowing designers to interact with models before production.
By leveraging tools like Computer-Aided Design (CAD), professionals can transform 2D images into three-dimensional models. This helps rapidly prototype assemblies and identify pain points virtually, reducing the need for physical prototyping, which saves time and costs. The versatility of computer vision in product design can be applied to numerous industries, from automotive to apparel.
Example: Nike utilizes CV to help their customers find the perfect fit. An app powered by computer vision lets users scan their feet, capturing 13 visual data points to create a tailored 3D model that helps consumers select the most accurate shoe size. This aids the company in minimizing returns.
Quality Control
Consistent control over quality is an Achilles heel of many manufacturing organizations, pushing them to find innovative ways to establish robust monitoring. Computer vision enables a sophisticated defect detection system, using cameras to scan and identify irregularities within products. It’s a significant step up from manual inspection as the algorithms are precise and work in real-time.
A system like this can accurately assess dents, scratches, incorrect assembly, damages, and so on and alert workers about defects before the products proceed through the production line. Advanced CV-driven systems can also analyze the root causes of defects, evaluating whether they occurred during manufacturing or were caused mechanically because of mishandling.
Example: Samsung is a prominent user of computer vision that oversees the quality control of its printed circuit board manufacturing. The company’s dedicated system analyzes a vast number of images to detect imperfections that may be otherwise overlooked.
Automated Assembly
Robotized assembly lines are not a concept of the distant future anymore, and with the help of computer vision in manufacturing, corporations can substantially automate this process. Robotics powered by CV excel at tasks like picking and placing parts into individual components, as well as combining these components into finished products.
The visual nature of this technology guides robots through the positioning and orientation of objects, informing them about sizes and shapes. This enables them to assemble pieces correctly and demonstrates an exceptional level of precision, even in complex or multi-component products. Visual identification of flawed parts driven by CV further promotes a cooperative approach between human supervisors and robots.
Example: Tesla is a famous example of leveraging computer vision in automated assembly, allowing robots to put together vehicle components. Their newest “unboxed” approach is set to reduce manufacturing costs by as much as 40%.
Sorting and Segmentation
In industries like industrial waste recycling, food processing, pharmaceuticals, and more, accurately sorting parts and materials is essential to consistent operation. CV-powered systems can evaluate large quantities of images and videos to segment and separate items properly based on their characteristics, sizes, materials, colors, etc.
Computer vision for manufacturing and sorting can become an indispensable tool for categorizing raw materials or removing faulty items from the line entirely with minimal margins of error and enhanced speed.
Example: a company called Recycleye is using computer vision to classify types of waste and employs robotics to sort it automatically with unprecedented accuracy.
Predictive Maintenance
Faulty or aging equipment is one of the leading causes of downtimes in manufacturing, which leads to financial losses. A survey by Limble demonstrates that 67% of responding manufacturing organizations are finding a solution to this problem in predictive maintenance. What is a more effective approach? Predictive maintenance powered by computer vision.
Such systems can use visual cues to identify early signs of machinery wearing down or degrading, such as anomalies in operations, corrosion, cracks, and so on. It facilitates a proactive approach to maintaining equipment before it goes out of order and reduces downtime that is spent on fixing or replacing the machinery.
Example: Shell, a global energy company, is using CV in predictive maintenance to oversee the condition of machinery and pipelines in their refineries. It helps them extend the operational life of their assets and increase equipment reliability.
Regulation Compliance
One of the broader computer vision use cases in manufacturing covers compliance with regulations. Many industries, such as pharmaceuticals, enforce complex regulatory standards that a CV system helps monitor and navigate. It can be employed to oversee quality checks like proper packaging, accurate labeling, correct handling, and more.
In hazardous environments, computer vision can facilitate adhering to hygiene protocols, which is crucial for both the product’s and the manufacturing process's safety.
Example: Pfizer utilizes computer vision in their chemical workup processes, assisting human chemists in ensuring the procedure follows regulatory standards.
Worker Safety
Safety in the workplace is among the major concerns that manufacturing corporations are striving to mitigate as efficiently as possible. CV-driven systems can detect dangerous behaviors or recurring anomalies that typically lead to injuries and raise the alarms before risks escalate.
For instance, such a system can identify when a worker is not wearing or improperly wearing personal protection equipment (PPE) in hazardous areas. Or when a worker is misusing machinery, which can lead to severe accidents. Suspicious behaviors that the computer vision systems are trained to detect, like accessing restricted areas, can also be flagged and reported, preventing situations that put the safety of a worker in jeopardy.
Example: Another instance of Shell using computer vision in manufacturing, this time to ensure workplace safety. A notable feature of their system is that it is capable of detecting when a person creates a highly hazardous precedent by lighting a cigarette near pumps. Staff is then alerted to close down the pumps to avoid devastating fires and explosions.
Inventory Management
Over the last decade, inventory management systems have become quite capable of efficiently monitoring warehouses and supplies. However, computer vision can majorly boost the productivity of managing items as it physically oversees the warehouse in real-time and analyzes its state.
CV systems can track stock levels, count inventory, identify missing or misplaced items, provide actionable insights for storage space optimization, and more. Moreover, with a mindful approach, you can seamlessly integrate computer vision into an existing inventory management program, preventing shortages and overstocking while reducing operational costs.
Example: Both Amazon and Walmart, retail giants that deliver billions of packages annually, leverage computer vision to make inventory management efficient. Amazon’s warehouse robot Sparrow uses CV to detect and handle individual products. While Walmart utilizes computer vision to track stock with more than 95% accuracy.
Supply Chain Optimization
One of the overarching computer vision applications in manufacturing is aimed at optimizing supply chains at various touch points. Visual systems can help streamline logistic processes and track inventory from production to warehousing to distribution. Autonomous warehouse vehicles, like forklifts, can also be powered by CV to minimize human supervision during transit.
Cameras equipped with computer vision tech can monitor for damaged goods, scan barcodes on production lines, evaluate more efficient routes, identify bottlenecks in the supply chain, etc. These systems can be a powerful tool in sectors like retail for fine-tuning the entire process and making sure packages get delivered on time without costly mishaps.
For example, as a logistics company, DHL finds value in computer vision in supplementing its transportation routes, tracking deliveries, and overseeing safety conditions.
Advantages of Computer Vision in Manufacturing
From the list of use cases you have learned so far, the extent of benefits that this technology can bring to the table should already begin taking shape in your mind. Indeed, the rapid adoption of computer vision into manufacturing by leading corporations and industry giants indicates a vast array of opportunities worthy of investment.
However, we don’t want to leave you guessing as to what kind of advantages you can gain by implementing CV into your business. Let’s highlight the most valuable ones and discuss their influence one at a time.
Increased Productivity
Computer vision manufacturing takes productivity to the next level by offering non-stop monitoring of crucial processes and helping identify inefficiencies in the workflow. Its ability to detect anomalies in the machinery and production labor leads to reduced downtimes, offering suggestions to optimize manufacturing outputs.
Production cycles can be significantly accelerated through process automation, with the CV-powered system handling tasks like assembly, inspection, handling, routing, and much more. Combining robotics with computer vision can speed up the completion of repetitive and time-consuming tasks, paving the way for a fully automated production line. Advanced systems do not require human intervention, enabling smart manufacturing supervision.
Elevated Quality Control
Controlling product quality becomes more precise and robust with computer vision. Through visual detection, a system can spot defects, irregularities, and damages in real-time, even the most minute divinations that are otherwise hard to identify with a human eye. This further minimizes the rate of error through manual inspection and sets a standardized approach to assuring the quality at manufacturing lines.
With CV, the corporation’s ability to adhere to regulatory measures is enriched with the capability of overseeing production processes and controlling the consistency of manufacturing. This is especially valuable in sectors like food processing and pharmaceuticals, where precision in sorting and packaging is integral.
Improved Workplace Safety
In workplaces where safety risks are heightened due to the nature of production, computer vision in manufacturing can aid in improving protection and security protocols. This includes the personal safety of workers as the system can be trained to detect visual signs of distress, fatigue, or discomfort that can lead to unfortunate and damaging injuries.
On top of that, CV systems can monitor whether employees are fully equipped in personal protection gear, maintain safe distances from hazardous environments, or properly operate the machinery. Unsafe behaviors can also be detected, alerting supervisors to take action before situations can escalate into accidents.
Cost Optimization
Companies can lower operational costs with computer vision in two ways: by reducing inefficiencies that lead to downtime and longer production cycles and by decreasing the need for manual assistance. The first is a direct influence of optimizing efficiency. Lowering the number of reworks due to robust defect detection and preventing expensive maintenance with predictive monitoring — all of this can cut costs.
The second way is downsizing labor costs, as CV systems can handle supervision at enhanced levels continuously without fatigue. Staff resources can be allocated to other areas where human insight and skill sets cannot be superseded by intelligent imaging.
Rich Analytical Insights
The value of computer vision for manufacturing can further be extracted in the form of rich analytical data. As the program learns about your production procedures and operations, it can generate useful insights to help your organization reach optimal strategies for improvement. Ultimately, this data is customized to your unique manufacturing specifications and work conditions.
What business owners gain in addition to other benefits is a competitive edge in an ever-demanding consumer landscape. Propelling innovation through actionable data is one of the core priorities of computer vision technology.
Key Challenges of Computer Vision in Manufacturing and Their Solutions
It would be an ideal world if innovative technologies only presented benefits and didn’t pose any challenges, but reality is a bit more complicated. When it comes to implementing and operating a computer vision-based system, corporations need to be aware of the common pitfalls that may await them and know how to navigate them properly.
Below, we want to go over the four most pressing issues with CV in production sectors and give you some advice on the ways you can mitigate these ongoing issues.
Access to High-Quality Data
At the core of computer vision applications in manufacturing is the data it is trained on. Frequently, companies struggle to find relevant and high-quality information either because it’s not publicly accessible or the organization itself lacks the necessary amount of data that will be suitable for training.
Poorly compiled datasets with insufficient information, improperly labeled outliers, and large volumes of noise can lead to the system not being accurate in its analysis and ultimately not meeting its established requirements.
Solution: for CV technology specifically, manufacturing companies can consider installing high-resolution cameras and surveillance equipment to capture diverse images. Consistent lighting and various angles will help gather as much valuable data as possible to use as a basis for training. There are also alternative information collection methods, such as generating synthetic data to fill the gaps in datasets.
High Implementation Costs
While computer vision is a very valuable technology to implement and can save expenses in the long run, it is also quite costly to initially invest in. It calls for advanced hardware, like cameras, sensors, processing units, and more, to meet operational demands. On top of that, developing customized software to support CV systems can be expensive, as the technology needs experienced developers for creation.
Maintaining and regularly updating the system is another cost factor, especially if retraining is needed to scale the model to account for expanding areas of operation or introducing new advancements.
Solution: there are computer vision use cases in manufacturing that don’t necessitate extremely high-end equipment, which can be a starting point to test out this technology in real-world practices. Opting for pre-made software solutions can also alleviate the strain on the budget, as opposed to completely personalizing the system.
Complexities with Integration
Integrating computer vision into existing systems requires a holistic approach, as compatibility issues can be a detrimental point. A uniform system has to account for different types of machinery and production lines to operate properly. Legacy software, in general, can be incompatible with CV integration, calling for a complete rehaul.
Training the staff to operate new systems can be a further challenge, especially if it covers some level of automation, like assisted assembly.
Solution: an existing system audit will reveal areas where compatibility can be an issue with computer vision manufacturing, allowing you to create a plan for addressing those challenges directly. Certain IoT platforms can also gradually introduce new technology to older systems without initial disruption.
Interpretability Issues
In algorithms like deep learning, which are at the core of CV, there is a persistent issue of explainability. Since the algorithm is supposed to mimic human brain activity, interpreting some of its outputs can be a challenge. The so-called black box AI problem can make it difficult to understand why the system came to a certain conclusion.
Computer vision in manufacturing industry is not exempt from this problem and can cause trust to be hindered if there is no clear explanation as to how and why the system triggers a specific activity.
Solution: explainable AI (XAI) techniques are at the forefront of combating this problem, leveraging models that offer concise interpretability of its decision-making. In CV, the system can visually highlight how it identifies and processes objects. Human oversight may also be necessary to monitor the computer’s judgment and make adjustments if the decision is hard to verify.
Leverage Computer Vision with Tensorway
The manufacturing industry demands technological advancement to streamline productivity and optimize workflows. For this reason, computer vision has gained traction in recent years and is not showing signs of slowing down. As more corporations adopt CV, it’s important to stay on top of this trend to maintain your competitiveness.
Integrating computer vision in manufacturing requires a mindful approach and a clear understanding of all the nuances associated with this field of artificial intelligence. Hence why, in our guide, we have explained how this tech is shaping the sector, its most popular use cases, benefits, and challenges.
If you would like to start transforming your manufacturing business with CV, be sure to contact us. Our team will deliver a solution tailored to your individual goals and seamlessly integrate it into your existing system.
FAQ
How is computer vision used in manufacturing?
There are diverse computer vision applications in manufacturing aimed at increasing productivity and optimizing production cycles. CV technology can be used to control the quality of products by standardizing manufacturing and detecting defects. The sensors employed in CV-powered cameras enable the automation of processes like assembly as they recognize objects and their positioning.
In the workplace, computer vision can monitor safety procedures, alerting staff to hazardous situations. The system can also oversee the state of machinery, providing early assessments of equipment wear down and encouraging preventative maintenance. Additional use cases include supply chain optimization and inventory management, making CV a versatile tool in manufacturing.
Which manufacturing industries can apply computer vision?
Computer vision can be applied to numerous industries, as its core functionality is to overview the production lines. Companies in sectors like automotive can use CV to facilitate automated assembly. In pharmaceuticals, computer vision can help with package inspection and workplace safety. Electronics manufacturers can apply CV to detect defects in microchips and control the quality.
There are many computer vision applications in manufacturing that companies from all kinds of sectors can leverage to their benefit.
What technologies power computer vision systems to improve production quality in manufacturing?
CV technology is powered by advanced deep learning algorithms, specifically convolutional neural networks. They allow high-resolution cameras and sensors to capture and process images, extracting valuable insights. This equipment can visually understand the context and unique identifiers of the objects in the image, enabling defect detection and deviations from the quality standards.
Computer vision in manufacturing is regarded for its high accuracy in recognizing inconsistencies within a product or components, enabling proactive quality control.
