Computer Vision for Quality Inspection: Boosting Accuracy and Efficiency

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Digitalization in the business world is not a choice but rather a necessity. Whether your business operates in manufacturing, automotive, or agriculture, using computer vision (CV) can undoubtedly increase operational efficiency and drive growth.

As businesses constantly strive to meet standards while minimizing costs, computer vision for quality control can kill two birds with one stone. The automated quality inspection process significantly increases a company’s productivity by reducing human error and optimizing operations. 

This article will analyze how computer vision is used for quality inspection and highlight the key business benefits.

Article Highlights:

• Image analysis with computer vision (CV) can predict potential faults before they occur;
• CV solutions developed by CHI Software can be easily integrated into various production environments and do not require a complete system overhaul;
• Quality inspection powered by algorithms is particularly beneficial for manufacturing, retail, automotive, and agriculture;
• The cost to build your own CV model for quality inspection can vary from USD 20,000 to around USD 145,000.

Usage of Computer Visions for Inspections Across Industries

In one of our previous articles, we discussed the possibilities and significance of computer vision (CV) in detail. It is a rather complex process that can be compared to human vision, giving machines “digital eyes” that allow them to understand visual content by capturing images, processing them, extracting and classifying the necessary features. Thanks to the continuous execution of these steps, computer vision can detect defects and anomalies in real time, track and analyze the movement of objects, search for images, and so on.

How businesses can use computer vision for regular checks

Computer vision development services are used in various industries to solve complex problems. Here are some typical applications that demonstrate CV’s versatility:

• Deciphering text: CV technology interprets text from labels or instructions, even if the text is damaged. This can be useful in logistics and documentation.
• Predictive maintenance: CV can predict potential malfunctions before they occur by analyzing images. A prime example of such an application is our case study of scanning vehicles for hail damage.
• Recognition and classification of objects and patterns: CV systems identify and classify different objects in images or videos according to specific features.
• Quality control: Quality inspection with computer vision involves the detection of defects in products during the production process. First and foremost, CV-based quality control guarantees that only high-quality products reach consumers.
• Automated inventory management: CV helps to track and manage inventory by automatically counting and identifying items.
• Face and gesture recognition: CV is used for facial and gesture recognition, which is vital for secure authentication.
• Object tracking: CV can be used to track the movement of objects in real time, for example, to monitor vehicles or people 
• Video recognition: CV can analyze and classify video content by recognizing scenes, objects, or actions in the frame.

Artificial Intelligence (AI) and the Evolution of Car Inspections Read more

When implementing computer vision for quality inspection, we offer this table of how its advantages can be applied in various industries.

Real-World Application: Vehicle Quality Inspection

Thanks to our years of experience, we have an example that can demonstrate visual quality control using artificial intelligence. We are ready to share the strategies for its development and application in business.

CHI Software’s Proven Solution for Predictive Maintenance

AI-based machine condition monitoring software is one of the solutions that CHI Software developed for a client from Israel. The main problem in this case was the high cost of vehicle maintenance due to constant and unpredictable seals. The solution was, therefore, clear: fault prediction and detection through continuous condition monitoring. 

Most machine health monitoring solutions rely on diagnostic fault codes. However, сodes do not simplify the process, as they only identify a problem after it has occurred. In addition, the available information is usually limited. 

Features of AI-based machine condition monitoring software developed by CHI Software

Our solution was to provide AI-based quality inspection because algorithms can analyze the information received from various sensors. This approach sped up the diagnostic process, and made it more accurate and available in real time.

The software solution we provided has the following characteristics:

• Faster selection of an anomaly detection model for each new customer data set;
• A robust data augmentation system that can be customized for different customer data sets;
• A machine protection system that recognizes unknown types of anomalies; 
• Detecting anomalies in offline and online broadcasts;
• Predicting breakdowns and helping to identify the cause of the problem.

Are you still looking for effective quality inspection tools? Computer vision can be an answer! Let's discuss your business needs

Expert Insight: Scaling Computer Vision Solutions

Rolan Akhmedov, Computer Vision Tech Lead at CHI Software assures: “Even though the example above is from the automotive industry, visual inspection using computer vision with AI can change the game in various industries, including agriculture, construction, and manufacturing. 

Here are a few tips to make the whole implementation process easier to understand. First of all, even the most significant projects can be scaled up with a pilot project. You can test your computer vision model on specific use cases and proceed to full-scale implementation. 

Also, an often overlooked option is using pre-trained computer vision models where possible. Pre-trained models can be implemented for regular tasks such as object detection, significantly reducing development time and complexity.”

Why Automate Your Quality Inspection with CHI Software

One of the principles of CHI Software is to go beyond just implementing AI. What does this mean? The following section will list our company’s advantages in detail and prove the main idea with life hacks for successful development processes.

Visual Quality Inspection with AI: Beyond Simple Implementation

At CHI Software, we offer more than just automated quality inspection with computer vision. We deliver immediate benefits by integrating different AI innovations into your operational infrastructure.

CHI’s computer vision developers have created:

• a system for scanning vehicles for hail damage that uses computer vision to detect and assess hail damage to vehicles;
• a face recognition solution for photographers to help easily identify different people in images and their faces, optimizing photo management and delivery to customers;
• a computer vision solution for a cosmetic retailer that analyzes customers’ faces and skin types, suggesting them beauty products;
• image recognition software for kindergartens to track children’s activity, monitors their attendance, and ensure that they are picked up only by authorized persons;
• an automated building approval system that analyzes architectural plans and checks compliance with local regulations using artificial intelligence; 
• vehicle condition monitoring software for tracking vehicle health in real time, using sensors and artificial intelligence to identify potential problems; 
• an AI-powered sports coach that analyzes athletes’ performance through video and input, and more.

Want to learn more? Check out our case studies for real-world examples.

See our portfolio

Our team has experience integrating AI solutions into core systems such as ERP, HMI, WMS, PLM, and SCADA. Why is it important? Earlier, we described examples of how to implement NLP in ERP systems, focusing on the main advantages offered by these innovations. 

Like any of our projects, quality inspection with computer vision solutions from CHI Software are scalable and do not require a complete system overhaul. To prove it, we’ve described the role of computer vision in business automation and shared tips on how to seamlessly integrate CV technology into your existing system. 

One of CHI Software’s key strengths is its ability to customize solutions to the specifics of your product or industry. The benefits of customization come from working closely with clients to thoroughly understand their business needs and industry requirements. Our team also pays special attention to selecting the most appropriate algorithms, training models on specific client data, and integrating them into existing systems. And of course, after the solution is deployed, we provide ongoing support, regularly optimizing it as new challenges arise.

Practical Tips from CHI Software for Handling Computer Vision Challenges

Implementing AI-assisted visual quality control in different industries comes with several challenges. Here are the most common roadblocks, followed by practical tips from CHI Software on overcoming them.

Common pitfalls in computer vision solutions development

Lack of Data to Train the Model

CV systems require large and accurately labeled datasets to train their models on. To meet this need, you can pursue synthetic data generation techniques or augment existing datasets. Even by slightly altering the existing data (rotating, scaling, or changing the color), you can effectively ‘augment’ your dataset without collecting additional data.

Comprehensive System Integration

Implementing CV-based quality control systems into existing production workflows can be challenging because inconsistencies have the potential to disrupt the entire system.

We recommend you opt for modular, API-based solutions for easy integration with existing systems. In our experience, using middleware that connects CV systems to your existing infrastructure is also a practical approach that avoids the need for a complete rebuild.

Real-time Processing

Real-time quality inspection requires fast, accurate processing of large amounts of visual data. Sometimes, a volume of data can overload the system, leading to slower performance.

To solve this problem, we suggest implementing edge computing for real-time image processing at the data collection stage. Edge computing reduces the need to send large amounts of data to a centralized server and can significantly improve processing speeds. 

Trust CHI engineers to cope with these challenges for you Message us to discuss your project

Quality Inspections with Computer Vision: Typical Features and Development Cost

Computer vision has many characteristics that can help with several quality control process tasks. In the next section, we’ll look at the most valuable ones and the factors that influence the development price. 

Key Features of AI-Driven Computer Vision for Quality Assurance

Here are some typical features of these CV systems:

Key features of computer vision for quality assurance

• Image сapture and preprocessing

The system captures high-resolution images, which is essential because it allows detection of even the most minor product defects. 

The next component is pre-processing: improving the image quality before the analysis begins, which is necessary to guarantee the accuracy of the data.

• Data management and analytics

Correct labeling and data management is critical – it helps to improve the validation process over time. In addition, reviewing historical data and analyzing past inspections can reveal recurring defects or trends.

In this way, computer vision in quality control helps companies identify problems, improves their processes, and significantly reduces the number of defects in the long run. 

Anomaly Detection with Computer Vision: Enhancing Quality Inspection Across Industries Read more

• Scalability and flexibility

Customizable inspection pipelines allow manufacturers to quickly adapt their systems to different types of products or defects.

Additionally, hybrid edge computing systems are becoming popular for large-scale operations, so they are widely used in the retail and automotive industries.

Finally, to ensure that all systems can process a large amount of information in real-time, it is worth implementing cloud solutions. This scheme ensures that everything runs smoothly, regardless of scale.

• Reporting dashboards provide essential information such as defect rates, root cause analysis, and information on compliance with industry standards.

To illustrate how all of the above features work in real life, we can refer to our article demonstrating the details of AI vs manual inspection, which is based on our case study of work we did on developing a vehicle scanning system for inspecting for hail damage. By introducing CV to existing systems, higher-quality analysis using photos and previously recorded visual data became a possibility. As a result, automated quality inspection with computer vision can detect different levels of damage on different vehicles, and companies in turn increase their operational efficiency.

To make it more convincing, we’ll introduce a few examples of well-known companies that use computer vision in manufacturing quality control. But remember, this is just a tiny part of the massive number of businesses already taking advantage of CVs.

• BMW Group relies on automated image processing systems (computer vision) to detect defects in production and quality control.
• Amazon uses computer vision not only for quality inspection, but also for automatically tracking goods.
• Foxconn, powered by CV and unsupervised learning, checks tablet size and color in real time, automatically rejecting defective products.
• PepsiCo uses computer vision to scan each piece of its snacks as they come down the conveyor. Once a mismatch is detected, the computer can react instantaneously to adjust the temperature or other parameters of the conveyor.
• Renault Group has confirmed that computer vision ensures conformity and traceability during factory assembly reliably and efficiently.
• P&G Group actively utilizes computer vision to collect data from sensors and images to replace manual quality checks with autonomous operation.

Cost Drivers in Computer Vision Solutions

Computer vision development services span several categories. The costs of vision-based quality inspection vary greatly due to several factors. Here is a detailed breakdown:

• System Complexity

Systems that need to analyze multiple types of products or defects require more complex algorithms and training data. The cost of a basic CV solution can start at USD 20,000.

• Customization

Customized models require additional data collection and training, so the cost may vary. Depending on the specifics and the necessary level of integration, the development cost can start from USD 30,000.

• Hardware Requirements

For example, high-quality cameras can cost from USD 2,000 to USD 10,000. At the same time, the necessary computing infrastructure can add another USD 5,000 to USD 20,000.

• Integration with Existing Infrastructure

Designing the software to be seamlessly compatible with your existing infrastructure can cost USD 10,000 or more.

• Licensing and Software Costs

Apart from the development process, licensing fees for third-party software, frameworks, and specialized AI tools can add to the overall cost. Software licensing, image processing libraries, and machine learning platforms can start at USD 5,000; recurring subscription fees can add to the total cost over time. 

In total, developing a robust computer vision solution for your business will cost between USD 20,000 and USD 145,000. 

Get a rough cost estimation of your development project from our tech team Book a call with our specialists

Conclusion

Сomputer vision in manufacturing quality control is a technology that will significantly improve the efficiency of a company in several areas. CV has many advantages and features — but they must be skillfully implemented into the existing business ecosystem.

With developments such as the vehicle hail scanning system, CHI Software has proven its professionalism and ability to create a revolutionary new product using computer vision models.

Want to bring the power of computer vision to your business? Contact CHI Software today, and let’s build the future of AI-powered innovation together!

About the author

Alex Shatalov Data Scientist & ML Engineer

Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.