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A tutorial on building a real-time object detection and tracking pipeline for robotics using ROS 2 and YOLOv11, covering threaded inference, ByteTrack integration, confidence validation, and ONNX export for edge deployment.
This paper presents ARGO, a smart eyewear platform for on-device machine learning that integrates a multimodal sensor suite and an optimized YOLOv11 model for real-time urban obstacle recognition, achieving privacy-preserving local processing with low latency and a memory footprint of 2.483 MB.