1. The camera is integrated into the system to capture visual data, enabling real-time monitoring of environments and providing images or videos for further processing. Support for camera installation is offered.
2. The system automatically collects image data by continuously capturing images or videos from the camera's perspective. This occurs in real-time and without manual intervention, ensuring a consistent data stream for analysis.
3. The collected image data is used to train the AI model. This involves processing and analyzing the images so the AI can identify patterns and learn from them. The AI is then tested and evaluated to ensure accuracy and performance, confirming it functions correctly.
4. The AI model is quantized to reduce its size and optimize computations. This process facilitates deployment on resource-constrained devices without compromising performance.
5. The optimized AI model is integrated into a microcontroller or microprocessor unit. This enables the model to run directly on devices with limited memory and computational power, allowing for real-time decision-making.
6. The system delivers real-time results that are immediately available after processing the image data.
The Data Spree AI Rubber Material Check is a demo application for the Renesas EK-RA8D1 development kit, enabling precise, automated optical classification of rubber materials. Powered by the R7FA8D1BH2CBD chip, the application demonstrates how TinyML can seamlessly run advanced AI on compact, energy-efficient devices. By integrating AI classification software, the solution optimizes production workflows, enhancing efficiency, quality, and process speed.
This application enables the EK-RA8D1 to perform real-time decision-making directly on the device. Local data processing minimizes latency, ensures data security, and reduces dependency on cloud infrastructures. By combining the efficiency of Embedded AI with the reliability of Renesas hardware, the solution provides a scalable, cost-effective system tailored to industrial needs.
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