# Low-light Object Detection

Source: [Low-light Object Detection](<https://www.sota2.com/research/paper/68fa73221e209131cea5a92a>)

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

Data updated: 2025-10-26T02:42:27.853Z

Authors: Pengpeng Li, Haowei Gu, Yang Yang

Published: 2024-05-06T14:36:01.000Z

## Abstract

In this competition we employed a model fusion approach to achieve object detection results close to those of real images. Our method is based on the CO-DETR model, which was trained on two sets of data: one containing images under dark conditions and another containing images enhanced with low-light conditions. We used various enhancement techniques on the test data to generate multiple sets of prediction results. Finally, we applied a clustering aggregation method guided by IoU thresholds to select the optimal results.

## 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/2405.03519>)

[PDF](<https://arxiv.org/pdf/2405.03519.pdf>)