# Automatic Labelling for Low-Light Pedestrian Detection

arXiv: https://arxiv.org/abs/2507.02513 · HTML: https://mlsanity.com/paper/2507.02513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2112.08088/) (2021)
- [WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild](https://mlsanity.com/paper/1909.12118/) (2019)
- [Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving](https://mlsanity.com/paper/2404.04804/) (2024)
- [TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection](https://mlsanity.com/paper/2305.16580/) (2023)
- [Pedestrian Detection in Thermal Images using Saliency Maps](https://mlsanity.com/paper/1904.06859/) (2019)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)
- [Unsupervised Domain Adaptation for Multispectral Pedestrian Detection](https://mlsanity.com/paper/1904.03692/) (2019)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)

## Abstract (arXiv)

Pedestrian detection in RGB images is a key task in pedestrian safety, as the most common sensor in autonomous vehicles and advanced driver assistance systems is the RGB camera. Low-light pedestrian detection lacks large public datasets and autolabelling pipelines. This research proposes a solution in the form of an automated infrared-RGB pipeline. The pipeline consists of 1) Infrared detection, where a fine-tuned model for infrared pedestrian detection is used 2) Label transfer process from the infrared detections to their RGB counterparts 3) Training object detection models using the generated labels for low-light RGB pedestrian detection. The research was performed using the KAIST dataset. For evaluation, three object detection models, DETR, YOLO, and RCNN, were trained on generated and ground truth labels. When compared on previously unseen images, the results showed that the models trained on generated labels out-performed the ones trained on ground-truth in 5 out of 6 cases for the mAP@50 and LAMR metrics, and outperformed ground-truth on mAP@50-95 in all cases. Acquired results indicate that the proposed auto-labelling pipeline could be used for scalable annotation of low-light datasets for pedestrian detection. The source code for this research is available on GitHub: https://github.com/BouzoulasDimitrios/IR-RGB-autoamed-low-light-pedestrian-labelling