Artificial Intelligence Technology
Offline training and testing models use a huge image type data set.
Figure 1
driving cars as an example, computer vision is used to detect and classify objects on the road. It is also used to create 3D maps and estimate movement around. By using computer vision, self-driving cars gather information from the environment using
Figure 2
cameras and sensors, which then interpret and analyse the data to make the most suitable response by using vision techniques such as pattern recognition, feature extraction, and object tracking. In general, embedded vision can serve
Applying the model to a real- world sample image to detect on the tested model from phase1.
Display results and analysis, in addition to predictions and suggested solutions.
many purposes, and these functionalities can be used after customisation and the needed training on different types of datasets from many areas. Functionalities include monitoring physical area, recognising intrusion, detecting crowd density, and counting humans or objects, or animals. They also include identifying people or finding cars based on car numbers, detection of motion, and human behaviour analysis in different cases.
Case study: Agricultural plant disease detection
Vision AI and deep learning may be employed to detect various anomalies - plant disease detection is one example of this type of system. Deep learning algorithms - one of the AI techniques - are used widely for this purpose. According to research, computer vision gives better, accurate, fast, and low-cost results, as compared to the costly and slow labour- intensive results of previous methods. The process that is used in this case study can be applied to any other detection. There are three main steps for using deep learning in computer/machine vision (see Figure 1):
Step one is performed on normal computers in the lab, whereas step two is deployed on a microcontroller at the endpoint, which can be on the farm. Results in step three are displayed on the screen on the user side. Figure 2 diagram shows the process in general.
Conclusion
We are experiencing a revolution in high- performance smart vision applications across a number of segments. The trend is well supported by the growing computational power of microcontrollers and microprocessors at the endpoints, opening up great opportunities for exciting new vision applications. Renesas Vision AI solutions can help you to enhance overall system capability by delivering embedded AI technology with intelligent data processing at the endpoint. Our advanced image processing solutions at the edge are provided through a unique combination of low power, multimodal, multi-feature AI inference capabilities. Take the chance now and start developing your vision AI application with Renesas Electronics.
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www.cieonline.co.uk sales@camdenboss.com Components in Electronics December/January 2023 27
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