Computer Vision & Recognition Systems - Decoding the Visual World
Computer vision systems and recognition systems must process large volumes of optical sensor data in real time, making them another category of product that can benefit from artificial intelligence (AI) in general, and machine learning (ML) in particular. Sensors feed processing systems with unstructured data – data that is unformatted. AI/ML is particularly adept at finding patterns in unstructured data and developing predictive models it can subsequently refine as it processes additional data.
Some of the most common use cases for AI/ML in computer vision systems include:
- Smart Cameras
- Quality control in manufacturing (DVI)
- Autonomous systems
Key Computer Vision and Recognition Applications
Bringing AI to Defect Visual Inspection
Are you looking to automate your visual inspections and improve the efficiency of your operation? Maybe you’ve already explored some options but have simply been priced out by the existing solutions on the market, or you’ve been put off by the complexity of the setup on existing solutions. Thankfully, Avnet Silica’s AI-based solution removes complexity and is cheaper compared to existing solutions on the market!
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Smart Cameras
Cameras for safety monitoring are being installed seemingly everywhere – office buildings, retail outlets, entertainment venues, municipal locations, residential porches. Not all are AI-enabled, but AI/ML is gradually being included in a growing percentage of cameras. With AI, camera operators can do anything from distinguishing employees from visitors to identifying whether the person ringing the doorbell is a neighbor or a mail carrier.
Autonomous Systems
Autonomous systems include robots, cobots, drones, agricultural equipment, trucks, cars, and more. All of these systems incorporate optical sensors for navigation. Safe navigation is dependent on object detection and identification and that is best provided by AI/ML systems. Some of these systems have other purposes that also rely on visual systems. Agricultural harvesters, for example, need to be able to make a distinction between unripe and unripe fruit, while also being able to detect blemishes and disease. Again, identification and categorization are processes that AI excels at.
Putting power first - The power management behind AI Vision
It’s not surprising that the power-related and power-intensive components contribute considerably to the complexity and total costs of an AI-based video security system. These include the cost of purchasing and the ongoing operational cost. The semiconductor industry, Avnet Silica and its supplier partners are addressing this. Power is now a critical factor for any application. There is no doubt that the general trend is a net increase in the amount of power we consume. This is balanced by the development of more energy-efficient solutions.
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Guide
Getting Started with Deep Learning for Vision Applications
Avnet Silica, in partnership with Deep Vision Consulting, has created a set of resources to help you create your own demo, and it’s easier than you think. All you’ll need is some Lego™ and our Deep Learning starter kit.
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