How Computer Vision Can Help You With Quality Inspection
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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.
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.
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.
When implementing computer vision for quality inspection, we offer this table of how its advantages can be applied in various industries.
Key Features of AI-Driven Computer Vision for Quality Assurance
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.
Image Capture 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.
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
Reporting provides essential information such as defect rates, root cause analysis, and information on compliance with industry standards.
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.
Want to learn about more examples and find out how much CV implementation may cost? Our original article will reveal the answers!
