Applications, Market Leaders, Image Acquisition Methods, and Current Trends
Computer vision, also known as machine vision, is a technology that allows machines to "see" and interpret images or video sequences in real time through advanced camera systems and AI-based software. This technology has evolved alongside AI and is now transforming industries such as manufacturing, healthcare, and logistics, among others.
What is a Machine Vision System?
A machine vision system uses cameras to capture images, which are then analyzed using advanced image processing algorithms. These algorithms can identify patterns, recognize objects, and make decisions based on the visual information collected. This allows machines to perform tasks previously reserved for humans, such as quality control, product classification, or defect identification.
Image Acquisition Methods in Machine Vision
Image acquisition is the first crucial step in any machine vision system. It involves capturing an image of the environment through a camera, which will later be processed and analyzed. There are different image acquisition methods depending on the application and the type of information needed. Here are some of the most common:
2D Cameras: 2D cameras are the most widely used in machine vision and capture flat (two-dimensional) images of objects or scenes. These images are analyzed for tasks such as shape recognition, dimension measurement, barcode identification, or surface defect inspection. They are widely used in the manufacturing industry for quality control or product classification tasks.
3D Cameras: 3D cameras capture images that not only contain information about an object’s height and width but also its depth. This method is used when a complete, detailed view of the object in three dimensions is needed, allowing for more precise measurements. 3D cameras are especially useful in advanced industrial applications such as complex part assembly, robotics, and quality control of irregularly shaped objects.
Thermal Cameras: These cameras capture images based on the temperature of objects. Thermal cameras detect the infrared radiation emitted by objects and generate an image that shows temperature differences. They are used in applications such as security, heat leak detection, infrastructure maintenance, and, more recently, body temperature monitoring in medical settings.
Hyperspectral Imaging Sensors: These sensors capture images at multiple wavelengths of the electromagnetic spectrum, beyond what the human eye can see. Hyperspectral sensors are mainly used in industries like agriculture, where they allow for the chemical analysis of crops, or in mining, where they help identify materials by their spectral signatures.
Processing Acquired Images
Once images are captured by the cameras, the next step is image processing, which may involve several stages depending on the complexity of the task:
Preprocessing: Captured images may contain noise or imperfections due to environmental conditions (lighting, shadows, etc.). Preprocessing improves image quality through techniques such as noise filtering, contrast adjustment, and optical distortion correction. These improvements are essential for obtaining better results in subsequent stages.
Segmentation: Segmentation is the process of dividing an image into several parts or regions, generally with the goal of identifying and separating objects of interest from the background. This is achieved with algorithms that detect object edges or analyze differences in color and texture within the image. This step is essential for tasks such as object counting or defect detection in a production line.
Analysis and Decision-Making: In this stage, the preprocessed and segmented image is analyzed using pattern recognition or artificial intelligence algorithms. AI systems can identify specific shapes, colors, or codes, or even make complex decisions based on the analysis of previously learned patterns. For example, in quality control, the system can compare captured images to an ideal model to detect deviations or defects in products.
Outputs and Automated Actions: Finally, the machine vision system generates an output based on the image analysis. This output can be a simple binary decision (approved/rejected) or a more complex instruction for a robot or automated system (move, sort, assemble). In many cases, the vision system is directly connected to industrial machinery that acts based on the visual analysis results.
Applications in Various Sectors
Manufacturing Industry: In the industrial field, machine vision systems are essential for inspection and quality control. With high-precision cameras, it's possible to detect defects in products, measure dimensions, and ensure products meet quality standards. They are also used for automated inventory management, where machines can recognize and count products in real time, improving operational efficiency.
Healthcare: In healthcare, machine vision is revolutionizing medical diagnostics. Image analysis systems, such as X-rays, MRIs, and ultrasounds, allow for more precise detection of pathologies. For example, algorithms can help doctors identify early signs of cancer in complex images.
Logistics and Warehousing: In logistics, machine vision systems are essential for product classification and automated warehouse management. Robots equipped with cameras and AI systems can identify and move products efficiently, optimizing space and reducing human error. This is crucial in industries with continuous product flow, like e-commerce.
Leading Companies in Machine Vision
Keyence: Keyence is one of the most recognized companies in the machine vision market. Their VS series, a vision system with integrated AI, is highly adaptable and allows for quick setup for different inspection types. This system includes tools based on both traditional rules and AI, making it possible to efficiently solve complex inspection tasks.
Cognex: Cognex specializes in machine vision solutions with high-speed, high-precision cameras, ideal for industrial environments where speed is critical. Their systems are widely used in electronics manufacturing, automotive, and logistics, where real-time processing is a key factor in optimizing operations.
Trends and the Future of Machine Vision
The rise of artificial intelligence has accelerated machine vision development, enabling more complex applications. In the near future, this technology is expected to integrate further into fields such as advanced robotics and augmented reality. Additionally, combining AI with large volumes of visual data will continue to improve system accuracy, making autonomous vehicles and more intelligent robots capable of interacting with their surroundings more autonomously a reality.
Conclusion
Grupo Madiver and machine vision are transforming entire industries by offering solutions that enhance efficiency, precision, and speed in critical tasks, providing flexible, advanced systems that allow companies to automate processes effectively. As technology continues to evolve, its impact is expected to grow, driving new innovations and applications.
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