Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
Read the original on arXiv Computer Vision →The paper examines how multi‑camera streaming perception systems perform on heterogeneous edge platforms that share resources with other workloads. Using two end‑to‑end pipelines on a single GPU–NPU platform, the authors show that isolated single‑stream evaluations can mislead deployment decisions: while the GPU pipeline appears superior in isolation, GPU‑local contention causes deadline misses that make detections stale and can reverse the preferred placement. The study finds that the NPU pipeline, though less accurate for small and medium objects, nearly matches the GPU on large objects, and that under high contention the best placement shifts from All‑GPU to All‑NPU, achieving a 5.2× improvement in worst‑stream sAP. The authors argue that evaluation metrics should include contention sweeps, deadline‑miss rates, and worst‑stream sAP in addition to mean sAP to capture severe single‑stream degradation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.