Imago: Quality Control with Machine Learning and Computer Vision

Leveraging Machine Learning and Computer Vision to achieve controlled quality

Ensuring that each product meets predefined quality and approval standards is a goal of all manufacturing companies. Technology is the most efficient ally for quality control in production processes. In this context, we have put at the service of Imago's customers advanced Machine Learning algorithms that, when applied to the company's machine vision systems, enhance the capabilities of identifying anomalies and deviations from standards.

Equipping monitoring tools with efficient algorithms in predictive analytics

Imago involved us in the development of new Machine Learning algorithms for use in video analysis systems of production processes. The goal of this innovation was the ability to offer the company's customers even more effective systems in the real-time analysis of production along the assembly line and in the precise detection of all kinds of anomalies at different stages of the process to highlight, predictively, potential breaks or defects in products and other deviations from the defined quality standards for production.

Proposed Solution

Computer Vision provides data, Machine Learning provides experience

For Imago, we specifically developed new Machine Learning models to be applied to systems for on-board optical controls. Thanks to the functionality of the new algorithms, the core system has developed a growing learning capability and a continuous increase in accuracy in predicting the impact on products of even minor anomalies in the production process. In fact, machine learning algorithms allow the system to benefit from learning from experience and, therefore, to improve over time, learning from the processed data and becoming increasingly accurate.
In this way, the system is able to predict the outcome of the production process and send alerts when there is a potential risk of breakage or when there are deviations from standard conditions, enabling timely intervention and avoiding damage, delay or waste.

Results

Quality assurance is the result of efficient monitoring activities

Thanks to the use of advanced algorithms, the solution we developed for specific applications in Imago's quality control and production tracking systems produced immediate benefits that were destined to grow over time. The solutions produced by Imago immediately showed an increase in performance, and the company can now offer its customers systems that enable significant improvement in the detection and analysis of anomalies along the assembly line with a consequent increase in production efficiency.

Solutions

Company

Investors

Imago - Computer Vision for Quality

Leveraging Machine Learning and Computer Vision to achieve controlled quality

Challenge

Equipping monitoring tools with efficient algorithms in predictive analytics

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Proposed Solution

Computer Vision provides data, Machine Learning provides experience

Results

Quality assurance is the result of efficient monitoring activities

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