# ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy

Source: [ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy](<https://www.sota2.com/research/paper/68fa730f1e209131cea5a300>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:40:28.783Z

Authors: JunKyu Lee, Blesson Varghese, Hans Vandierendonck

Published: 2022-10-28T12:06:29.000Z

## Abstract

This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.16083>)

[PDF](<https://arxiv.org/pdf/2210.16083.pdf>)