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Machine Vision Software Developer Accelerates Deployment of Deep Learning Technologies
As the demand for advanced image analysis continues to grow, an increasing number of machine vision software companies are integrating deep learning technologies into their offerings. With these firms continuously refining their deep learning tools and enabling more users to implement them successfully, the adoption of such technologies is becoming increasingly widespread across various industries.
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-driven 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. This deep learning-based platform is specifically tailored for inspection applications and has proven successful in sectors such as pharmaceuticals, medical devices, automotive, textiles, printing, and watchmaking.
Cognex emphasizes that deep learning complements traditional machine vision techniques. While geometric pattern recognition and edge detection remain essential for high-precision tasks like robot guidance or measurement, deep learning excels in scenarios where human-like judgment is required, such as quality inspection. By learning from examples, it eliminates the need for complex programming, making it accessible to a broader range of users.
In South Korea, Sualab recently launched its SuaKIT inspection software, which leverages real-world industrial image data to classify and detect defects. The software employs neural networks to automatically identify defect values using newly trained images of both normal and defective products, processing up to 1,000 images of size 2,048×2,048 within 30 minutes. Its user-friendly design allows even those without significant programming experience to train the system by simply entering defect data. Additionally, SuaKIT utilizes NVIDIA’s CUDA technology with high-performance GPUs to ensure fast and efficient processing.
According to Sualab’s enterprise group deputy manager, the integration of deep learning significantly reduces errors during testing. Combined with CUDA, this enables SuaKIT to maintain high performance even in fast-paced manufacturing environments.
Another leader in the field, MVTec, 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 achieve higher reading accuracy than previous methods. The latest version of Halcon also allows users to train convolutional neural networks (CNNs) for automatic image classification. According to MVTec’s product manager, customers can save time, effort, and costs by using self-trained models instead of traditional programming approaches.
For instance, users can identify defect classes by showing the system sample images, eliminating the need for complex coding. In industrial settings, deep learning is widely used for classification tasks such as product inspection and part identification.
CythSystems is another company focusing on deep learning for machine vision. Its NeuralVision software is designed for users without prior experience in machine vision. Unlike traditional systems that require programmers to select specific algorithms, NeuralVision learns by being shown examples of good and bad parts, along with variations in lighting, shadows, and environmental conditions. This approach enables the system to distinguish relevant features from irrelevant ones, improving its ability to recognize and classify objects accurately.
Overall, the integration of deep learning into machine vision software is transforming how industries perform quality control, inspection, and automation, making these processes more efficient, accurate, and accessible.