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Decoding Vision AI: AI machine learning models underlying visual Models



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In today's fast-paced world, industries like manufacturing are constantly pressured to increase efficiency, reduce costs, and improve quality. One technology that is helping to achieve these goals is Visual Inspection AI. This innovative technology uses artificial intelligence (AI) and computer vision to automate inspections, making them faster, more accurate, and more efficient.


How Does Visual Inspection AI Work?


The process involves capturing images or videos of products or materials using cameras and sensors. These images are then automatically analyzed using machine learning models trained on large datasets to identify patterns and anomalies. As the system processes more data, it becomes more accurate and efficient at detecting defects and anomalies. This means that a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, quickly surpassing human capabilities.


The Role of Computer Vision


Computer vision is a subfield of AI that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs and take actions or make recommendations based on that information. It involves training large neural networks with vast amounts of data to recognize and understand complex image patterns and features. Applications of computer vision include facial recognition, image recognition, and facial biometric software.


Facial Recognition Technology (FRT)


FRT is a popular application of computer vision that identifies human faces. It works by using a computer vision system to detect and identify human faces in an image. The system measures and maps parts of a face, including the shape and color of eyes, noses, mouths, and chins, and converts these measurements into nodal points. These nodal points are then written as code, called a faceprint or facial signature, which can be compared to other faceprint codes in a database of pictures.


Food Inspection and Other Applications


Food inspection is just one of the many inspection applications of AI machine vision. Some systems can use high-resolution cameras to inspect objects for minute imperfections that would be invisible to the naked eye. Additionally, it can perform repetitive tasks, such as counting and measuring, with greater efficiency and increased throughput. This allows machines to handle these tasks while workers apply their efforts to more complex jobs.


Training Computers to Recognize Objects


For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects. Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).


Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image. A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.


Deep Learning and CNN


One important branch of deep learning, CNN, is often used for image and video processing. CNNs differ from normal neural networks in that they contain the feature learning stage consisting of one or multiple convolutional and pooling layers. Each neuron in the convolutional layer is not connected to all the neurons in the next layer, reducing the computational overload of training a traditional fully connected neural network.


Combined Multi-technology Approaches


CNN, GAN (Generative Adversarial Network), and transfer learning all have their suitable domains. CNNs perform much better in the suthe unsupervised onepervised learning domain than in the unsupervised learning domain. Therefore, some scholars want to combine CNN with GAN, called deep convolutional generative adversarial network (DCGAN), which performs well in the unsupervised learning domain. The introduction of CNN into the GAN method breaks the gap between supervised and unsupervised learning. Its change relative to GAN is to use full convolution instead of full connection.





Conclusion


Visual Inspection AI is revolutionizing industries by automating inspections, making them faster, more accurate, and more efficient. By leveraging the power of AI, computer vision, and machine learning, industries can improve quality, reduce costs, and increase efficiency, ultimately leading to higher customer satisfaction and a stronger bottom line.


Sources:



Volkmar Kunerth CEO Accentec Technologies LLC & IoT Business Consultants Email: kunerth@accentectechnologies.com Website: www.accentectechnologies.com | www.iotbusinessconsultants.com Phone: +1 (650) 814-3266

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