Performing sophisticated object detection on constrained edge devices may seem daunting, but with the right design, it becomes a powerful distributed AI solution. Using k0s, the lightweight CNCF-certified distribution, and a deferred inference pipeline powered by YOLOv8, this project tackles the challenges of capturing and processing video frames across heterogeneous environments.
Leveraging k0s’s minimal resource footprint and streamlined orchestration, combined with a cloud GPU inference service, our architecture offloads intensive workloads from edge devices. A Go-based frame capturer with HTTPS protocol reliably transmit video frames to GPU instances for near real-time detection under bandwidth-constrained conditions. A web-based visualization layer then aggregates and displays inference results in real time.
Learn about implementing deferred inference pipelines with YOLOv8, orchestrating containerized workloads using k0s, optimizing GPU utilization for cost efficiency, and achieving low-latency edge processing. See how this architecture brings state-of-the-art computer vision to resource-limited scenarios, opening new possibilities for distributed AI deployments at scale.