As a demonstration of that, I made a small Pyhton Flask web service that is suitable to run on the Raspberry Pi. Using a basic web UI you can PUSH images to it to do object detection and classification.
While processing some validation images from the COCO dataset, the observed inference speed is about 400ms, do add another 150 ms to post-process the results. This makes about 550 ms for the full object detection, which sounds pretty acceptable to me. Given it runs on a Raspberry Pi4 and I made the postprocessing code to be readable, not to have optimal performance.
The full source code is available for download on github
https://github.com/brunokeymolen/movidius-inference-server
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ReplyDeleteA YOLOv3 inference server for Intel Movidius enables efficient, real-time object detection by offloading computation to edge AI hardware like the Neural Compute Stick 2. By using Intel’s OpenVINO toolkit, the YOLOv3 model is converted into an optimized Intermediate Representation (IR) format, allowing faster inference across resource-constrained devices. In such a setup, a lightweight server (often built using frameworks like Flask) can accept image inputs via APIs and return detection results, making it suitable for distributed edge AI applications. This approach significantly improves performance, with inference and post-processing handled efficiently even on devices like Raspberry Pi paired with Movidius accelerators. Additionally, integrating with OpenVINO Model Server enables scalable deployment using REST or gRPC APIs, allowing multiple clients to access object detection services in real time. For practical exposure and implementation, exploring projects such as Object Detection Projects For Final Year and Deep Learning Projects for Final Year helps learners build scalable and efficient edge AI solutions.
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