Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
Research reveals benchmarks overstate edge AI performance by 20-30% in real deployment. Edge-TSR, a continuous inference system on NVIDIA Jetson Orin Nano, integrates detection, tracking, and a lightweight temporal stabilization mechanism, recovering up to 10.16% accuracy over per-frame baselines. A 55-minute vehicular test achieves sustained 16.18 FPS within safe thermal limits without cloud offload.
[2606.17241] Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
[Submitted on 15 Jun 2026]
Title:Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2606.17241 [cs.CV]
(or arXiv:2606.17241v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.17241
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aditya Mishra Mr [view email] [v1] Mon, 15 Jun 2026 19:39:55 UTC (36,079 KB)
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