As more machine vision software companies integrate deep learning into their offerings, the adoption of these technologies is on the rise. With continuous development of deep learning tools and increasing success stories from users across various industries, it's clear that deep learning is becoming a key player in the market.
One such 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 to enhance image analysis. Their Cognex ViDiSuite includes three core tools: ViDiBlue for fixtures, ViDiRed for segmentation and anomaly detection, and ViDiGreen for object and scene classification. Designed specifically for inspection tasks, this software has proven effective in sectors like pharmaceuticals, automotive, textiles, and medical devices.
Cognex emphasizes that deep learning complements traditional machine vision techniques. While geometric pattern recognition and edge detection remain essential for precise measurements and robot guidance, deep learning excels in quality inspection and tasks that mimic human judgment. It eliminates the need for complex programming, as it learns from examples rather than relying on predefined rules.
In South Korea, Sualab recently launched SuaKIT, an inspection software powered by deep learning. This tool leverages real-world industrial image data and uses neural networks to automatically detect defects. It can process up to 1,000 images of size 2,048×2,048 within 30 minutes. The software is user-friendly, allowing even those with limited programming knowledge to train the system by simply inputting defect data. With support for high-performance GPUs via NVIDIA’s CUDA technology, SuaKIT delivers fast and efficient processing.
Sualab’s deputy manager noted that deep learning significantly reduces errors during testing. Combined with CUDA, SuaKIT achieves high performance, even in fast-paced manufacturing environments.
Meanwhile, German firm MVTec has also embraced deep learning, integrating it into its renowned Halcon and Merlic software. Since version 13, Halcon has included deep learning-based OCR, featuring classifiers that outperform traditional methods. Users can now achieve higher reading accuracy using pre-trained fonts. The latest Halcon update also allows CNN training, enabling automatic image classification. According to MVTec, this saves time, effort, and costs for customers who can train their own models without extensive coding.
In industrial settings, deep learning is widely used for classification tasks, such as product inspection and part identification. For instance, defect classes can be recognized through image examples, eliminating the need for complicated programming.
Another company, CythSystems, offers NeuralVision, a deep learning-based solution for product inspection and classification. Unlike traditional systems that rely on manually selected algorithms, NeuralVision learns from examples. By showing the system various images—such as good or bad parts, under different lighting conditions—it teaches the system to identify relevant features and ignore irrelevant ones. This makes it ideal for users without prior machine vision experience.
Overall, the integration of deep learning into machine vision is transforming the industry, making advanced image analysis more accessible and efficient across a wide range of applications.
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