Computer vision for quality control

Human Vision vs. Computer Vision

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

• 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.

• 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.

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.

Computer Vision for Defect Detection in Manufacturing

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.

A company can obtain various benefits from computer vision at different levels. Some of these include:

1. Automation: Reducing manual labor to inspect every product that comes through the production line and redirecting that talent to other areas.
2. Cost Savings for the Company: Identifying and rectifying issues helps minimize the quantity of defective products entering the market, contributing to resource savings.
3. Long-term Profitability: The initial investment often shows a substantial return by preventing additional costs, ensuring product quality, and maintaining brand reputation.
4. Objective Inspections: Because algorithm training is done under preselected criteria, anomalies can be classified seamlessly, aiding the tasks of control personnel.
5. Process Flexibility: Keeping production technology up to date is essential: Computer Vision can adapt to different environments and needs for this purpose.

Applications of Computer Vision

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

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

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

3. Logistics: It is beneficial for repetitive processes in packaging, picking, or quality inspection systems.

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

In the inspection of operations and their quality, the following functionalities stand out:

• Verification of personal protective equipment in hazardous environments
• Personnel facial recognition
• Object detection in industrial environments
• Identification of imperfections
• Geometric inspections
• Review of the product’s surface finish, color, and texture

Constitution of a Computer Vision (CV) System

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.

Stages of Quality Control and Defect Detection with Computer Vision

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

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

2. 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.

3. 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.

What is CNN or Convolutional Neural Network?

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.

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

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:

1. 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.

2. 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.

3. 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.

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.

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 for Efficient Productions

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.