{"slug": "beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception", "title": "Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception", "summary": "Researchers introduced Edge-TSR, a continuous edge inference system for roadside perception on the NVIDIA Jetson Orin Nano, finding that benchmark-centric evaluation overstates performance by 20-30% compared to real-world streaming deployment. Edge-TSR uses temporal inference stabilization to recover up to 10.16% classification accuracy while maintaining real-time performance, demonstrated in a 55-minute vehicular deployment at 16.18 FPS without cloud offload.", "body_md": "arXiv:2606.17241v1 Announce Type: new\nAbstract: Continuous AI inference on resource-constrained edge hardware introduces deployment effects that are largely invisible to conventional benchmark evaluation, including temporal instability in streaming video, thermal throttling under sustained load, and workload-dependent performance variability. We present Edge-TSR, a deployment-oriented continuous edge inference system for sustained roadside perception on the NVIDIA Jetson Orin Nano. Edge-TSR integrates detection, tracking, fine-grained classification, and a lightweight track-aware temporal stabilization mechanism that improves streaming inference consistency with negligible computational overhead. Our central finding is that benchmark-centric evaluation systematically overstates deployed edge inference performance. Across three state-of-the-art baselines, we observe consistent 20-30% relative degradation when transitioning from static-image evaluation to real-world streaming deployment. Edge-TSR addresses this gap through temporal inference stabilization, recovering up to 10.16% classification accuracy over per-frame inference baselines while maintaining sustained real-time performance under continuous operation. We evaluate the complete system under diverse real-world deployment conditions, jointly characterizing inference quality, latency, throughput, and thermal behavior during long-duration operation. A 55-minute vehicular deployment over a 26 km route demonstrates sustained operation at 16.18 FPS within safe thermal limits on a single embedded device without cloud offload. Our findings show that deployment-aware evaluation and temporal inference stabilization are necessary components of continuously operating edge AI systems intended for real-world sensing deployments. We release a sample annotated streaming video evaluation dataset and full system implementation to support reproducible deployment-centric evaluation.", "url": "https://wpnews.pro/news/beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception", "canonical_source": "https://arxiv.org/abs/2606.17241", "published_at": "2026-06-17 04:00:00+00:00", "updated_at": "2026-06-17 04:25:02.154118+00:00", "lang": "en", "topics": ["computer-vision", "ai-infrastructure", "ai-research", "ai-tools"], "entities": ["NVIDIA Jetson Orin Nano", "Edge-TSR"], "alternates": {"html": "https://wpnews.pro/news/beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception", "markdown": "https://wpnews.pro/news/beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception.md", "text": "https://wpnews.pro/news/beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception.txt", "jsonld": "https://wpnews.pro/news/beyond-benchmarks-continuous-edge-inference-for-fine-grained-roadside-perception.jsonld"}}