Smart rear view camera running on batteries with Siana System and e2ip

The Edge AI Sensing Kit was initially introduced to showcase the power of embedded vision intelligence, using the detection of vehicles and people as a compelling example for smart transportation. While the initial demonstration highlighted real-time detection of people, cars, trucks, buses, and bikes in urban environments, the underlying edge AI vision technology is highly versatile. Its capabilities extend far beyond transportation, with potential applications in smart homes, smart cities, room occupancy monitoring, and a wide range of context-aware automation systems.

• The Edge AI Sensing Platform Discovery Kit leverages the STM32N6 MCU to run a real-time object detection and counting model entirely on-device. Key benefits include:
• Low-latency performance (typically under 200 ms) for immediate, real-time inventory visibility
• Full edge processing, with no dependency on cloud servers
• Enhanced accuracy, with AI models trained to detect multiple product types under varying lighting and arrangement conditions
• Ultra-low-power consumption, enabling continuous monitoring without straining energy budgets
• Reduced operational costs, by eliminating the need for cloud servers, recurring data costs, or additional tagging infrastructure

A 5MP camera captures high-resolution images, which are processed by the image signal processing (ISP) subsystem. A downscaled 256×256 frame is sent to the neural processing unit (Neural-ART Accelerator) for fruit and hand detection. Bounding boxes are post-processed to count fruits by type and detect interactions. Events are managed by the Event Controller and logged with timestamps. In parallel, the ISP provides a 960×960 frame for video encoding via the H.264 encoder, which are streamed over USB or Wi-Fi. An IoT controller handles telemetry for remote monitoring.

The sensor used in this mockup is the Sony IMX335 5MP RGB:

• Input: 2592x1944
• IPS resize: 960x960
• FPS: 15

E2ip will supply additional extension boards with various sensors to meet the specific requirements of the application.

e2ip and Siana Systems enable rapid prototyping, allowing solutions to be quickly field-tested before full-scale production and deployment. Thanks to seamless integration with the STM32N6 and the broader ST ecosystem, from the Model Zoo to STM32Cube.AI for custom model deployment, the Edge AI Sensing Kit streamlines the entire development process for edge AI applications.