Input Resolution Matters: Real-Time Object Detection Latency

• URL: http://arxiv.org/abs/2609.12920v1
• Date: Fri, 11 Sep 2026 14:45:12 GMT
• Title: Input Resolution Matters: Real-Time Object Detection Latency
• Abstract summary: We model total latency as the convolution of preprocessing, inference, and postprocessing distributions.<n>Experiments with YOLOv11n on NVIDIA Jetson Orin NX show that resolution-aware parameterization can improve distributional approximation.
• Score: 8.030506166253398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We model total latency as the convolution of preprocessing, inference, and postprocessing distributions under a simplifying independence approximation, with selected stage parameters expressed as functions of source-image resolution. Under this assumption, the probability density of the total latency is the convolution of the stage-wise densities, and its cumulative distribution function (CDF) provides the distribution of end-to-end detection time. Each stage is modeled by a parametric distribution (e.g., Exponential, Erlang, Normal, Gamma), with parameters expressed as functions of the source-image resolution. Experiments with YOLOv11n on NVIDIA Jetson Orin NX using COCO2017 images across multiple resolutions assess the proposed models against fixed-parameter baselines using Kolmogorov Smirnov, Anderson Darling, and Cramér von Mises statistics. The results indicate that resolution-aware parameterization can improve distributional approximation in the measured setting, particularly for the more flexible Normal and Gamma models, while the quality of fit remains distribution dependent. Our contribution is a theoretically grounded and lightweight formulation for studying resolution-dependent latency distributions in a measured object detection pipeline.

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