Sparse Modeling Offers a Different Approach to AI in Low Power Embedded Vision Applications
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Artificial Intelligence (AI) has tremendous implications for visual inspection systems because it gives machines the ability to sort through data without needing much in the way of human input. However, there are some drawbacks to traditional AI for visual inspection. These include:
- Large amounts of data: Vision-based artificial intelligence systems process every part of an image comprehensively. This takes a lot of computing power, which can make the process slow, data-intensive, and expensive. This also makes AI systems a poor fit in embedded systems, which are an essential part of machine vision applications.
- Requires a lot of input: Artificial intelligence requires a lot of images in order to make accurate and reliable predictions. This takes a lot of time, energy, and data storage to accomplish. It also makes traditional AI systems very slow to train.
Sparse Modeling AI
Sparse modeling is an alternative AI system that requires less data because it focuses on identifying specific features rather than processing whole images. Sparse modeling can constantly adjust and retrain itself for different conditions like lighting, vibrations, or even completely different image compositions.
Because sparse modeling is less computer-intensive, it is an energy-efficient solution for embedded machine vision systems in many different industries.
Applications of Sparse Modeling AI for Machine Vision
Here are a few of the present applications of sparse modeling AI for machine vision:
- Medical industry: Sparse modeling AI can be used to more efficiently process visual medical imaging data, particularly x-rays. This ability of sparse modeling AI to focus only on the essential features of x-rays can help medical professionals identify injuries and defects much more quickly.
- Manufacturing: Sparse modeling AI can produce quality control visual inspection systems that require very few images to train – as low as 50 to start. This helps reduce the reject rate, which is critical for improving the bottom line for manufacturing companies.
Besides these specific applications, any company that currently employs a machine vision system may benefit from sparse modeling AI, particularly due to its ability to make predictions with far fewer resources than traditional AI.
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