A Convolutional Neural Network is fed with gigabytes to terabytes of data to enable it to learn autonomously. The client provides this data according to their quality specifications; the results will be more accurate, with more information. Finally, it is necessary to test its functionality.

Fully Connected (dense) Layers: Each node or neuron is connected to the previous layer’s nodes. They are located at the neural networks’ end, where regression and classification functions are performed.

Convolutional Neural Network belongs to deep networks, and their architecture consists of different layers: input, hidden (composed of convolutional and pooling layers), and dense layers. They are highly effective in pattern recognition and spatial data, making them associated with image recognition.

Now that we’ve established the characteristics that differentiate one from another, it’s time to discover how Computer Vision works for quality control in factories, production, or machinery.

Is it possible to detect defects in an entire large-scale production line? The answer is yes, thanks to the development we will discuss in this entry.

Pooling Layer: Layers designed to reduce spatial dimensionality. There are two common types: max-pooling, which calculates the maximum elements, and average-pooling, which calculates the average. Both can fulfill pooling function.

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Security: Computer Vision is an excellent alternative for controlling the entry and exit of individuals from a building.

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Automotive: The integration of robotic systems has optimized a segment of the assembly process, leveraging advanced recognition capabilities.

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Processing: The captured and digitized images are generally analyzed with artificial intelligence algorithms (convolutional neural networks or CNN). It is in this phase that anomalies are identified.

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Computer Vision Models can be valuable additions to quality control. They enable the automation of repetitive tasks, such as inspecting the production line. This prevents costly errors or omissions at the start or during production.

Piece rejection/approval: Finally, products are classified as functional or not. If they meet the complete standards, we can keep them; otherwise, they need to be removed. This process uses PLC Communication protocols on the LAN network.

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Computer Vision is a tool that effectively complements other inspection methods. Even with highly trained personnel or quality control systems, its implementation can be a significant improvement.

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Human Vision: Our eyes excel at capturing images; afterward, the brain allows us to conceptualize, sorting out subtle functional flaws. We determine whether a product passes or fails an evaluation thanks to learned associations.

Quality control has relied on our vision for a long time, which is valuable and expected to remain so, but prone to errors. This represents a problem in several areas, such as compliance with specifications, customer satisfaction, standardization, among others. In an increasingly automated and industrial world, we can see an advanced alternative with “digital eyes”: Computer Vision.

We have explored a landscape in which Artificial Vision-Based Systems represent a significant achievement for the entire industry. With the assistance of algorithms, inspection reaches more efficient productions that align with the market specific needs. We invite you to discover how advanced data processing can provide you with the ideal development for your short, medium, and long-term goals with the support of Autmix.

Computer Vision (CV) for Quality Inspection is versatile and highly useful in many fields and sectors, here is a breakdown:

Before answering this question, we need to know that Computer Vision (CV) is a subfield of Computational Sciences that typically employ Artificial Intelligence models to develop systems capable of understanding and interpreting visual content.

A Computer Vision System facilitates adherence to specific quality control criteria for each production line. We will address its operation and implementation in stages:

Due to our eyesight, humans can tell the difference between what is noteworthy and what can be improved. We perceive when a final product meets the appropriate characteristics for consumption or use and when it does not.

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Convolutional Layer: This is the main block of this type of network, where multiple filters are applied to the input image to detect patterns of information. It then extracts the unique features of each image, compressing them to reduce their initial size.

The human neurological system inspires this kind of system, is designed to process data with grid structures (matrix structure).

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Capture (Digitalization): Obtaining digital images through industrial cameras from multiple angles to capture all the product’s characteristics.

Convolution operations play a vital role in image processing with neural networks, reducing computational costs compared to dense neural networks.

This reduction seeks patterns in the image, creating a smaller matrix, and is repeated for each filter in the network until a more straightforward result is obtained for analysis.

Computer Vision and Human Vision function similarly. Both gather information from an image but with some differences. Let’s explore them:

To understand this process, it is important to know three different types of layers within a convolutional network, and we will primarily review three in a simplified manner:

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Computer Vision: In production, systems based on Computer Vision have a significant advantage in inspecting large volumes, such as verifying entire production lines. Hundreds or thousands of pieces can be observed quickly, providing continuous and consistent results throughout the inspection.

Food Industry: Objectively and numerically categorizes objects based on their shape, color, and properties with the help of a digital image.

A Computer Vision System is typically composed of various interrelated elements that work together to process and understand visual information. It consists of a camera that observes the production line and captures later processed images. These systems generally operate with neural networks incorporating deep learning algorithms to be more precise, efficient and reproducible.

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