As more machine vision software companies integrate deep learning into their solutions, the technology is gaining traction across various industries. With the development of advanced tools and successful implementation in real-world applications, deep learning is becoming an essential part of modern vision systems, driving innovation and efficiency.
One notable example is ViDiSystems, a company acquired by Cognex in 2017. Founded in 2012 by Dr. Reto Wyss, a computational science expert, ViDiSystems has developed AI-powered software that enhances image analysis by training systems to differentiate between acceptable variations and defects. Cognex's ViDiSuite includes three key tools: ViDiBlue for fixture detection, ViDiRed for segmentation and anomaly detection, and ViDiGreen for object and scene classification. These tools are tailored for inspection tasks and have been widely adopted in sectors like pharmaceuticals, automotive, textiles, and more.
Cognex emphasizes that while traditional machine vision techniques—such as geometric pattern recognition and edge detection—remain crucial for precise measurements and robot guidance, deep learning excels in scenarios where human-like judgment is required. It simplifies the process by learning from examples rather than relying on complex programming, making it ideal for quality control and defect detection.
In South Korea, Sualab introduced SuaKIT, an inspection software powered by deep learning. This tool leverages real-world image data from industrial settings and uses neural networks to automatically detect defects. It can analyze up to 1,000 images of size 2,048×2,048 within 30 minutes, offering a fast and efficient solution. The software is user-friendly, allowing even those without coding experience to train the system by simply providing defect samples. It also supports high-performance GPUs via NVIDIA’s CUDA technology, enabling rapid processing in high-speed production environments.
According to Sualab’s deputy manager, the integration of deep learning significantly reduces testing errors, especially in demanding manufacturing conditions. The combination of deep learning and CUDA ensures superior performance, making it a reliable choice for precision-driven industries.
Another leader in this space is MVTec, which has incorporated deep learning into its well-known Halcon and Merlic software. Starting with Halcon 13, the company introduced deep-learning-based optical character recognition (OCR), featuring pre-trained classifiers that improve reading accuracy. Additionally, the latest Halcon version allows users to train Convolutional Neural Networks (CNNs) for custom classification tasks. This enables faster and more accurate image analysis, saving time, effort, and costs for end-users.
MVTec’s product manager highlights that customers benefit greatly from using self-trained networks, as they eliminate the need for extensive programming. For instance, defect identification can be done through image references, making it accessible to non-experts. In industrial machine vision, deep learning is primarily used for classification tasks such as product inspection and part identification.
CythSystems is another player in the field, offering NeuralVision—a deep learning-based platform designed for users without prior machine vision experience. Unlike traditional systems that rely on predefined algorithms, NeuralVision learns by analyzing images and understanding what defines good or bad parts. By exposing the system to various conditions like lighting changes and environmental variations, it becomes adept at distinguishing relevant features from noise. This approach simplifies the inspection process and improves accuracy, making deep learning a powerful tool for modern industrial automation.
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Belt grinding is the grinding of workpiece surface with high-speed annular belt.
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Mirror surface grinding is a grinding method to achieve the optimum surface roughness. After grinding the workpiece, the surface roughness is not more than 0.01 micron, the light is like a mirror, can be clear imaging
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