# Enhancing Vehicle Detection under Adverse Weather Conditions with Contrastive Learning

arXiv: https://arxiv.org/abs/2509.21916 · HTML: https://mlsanity.com/paper/2509.21916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [Attentional Feature Fusion](https://mlsanity.com/paper/2009.14082/) (2020)
- [Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning](https://mlsanity.com/paper/2212.04994/) (2022)
- [Patch-level Representation Learning for Self-supervised Vision Transformers](https://mlsanity.com/paper/2206.07990/) (2022)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning](https://mlsanity.com/paper/2311.11646/) (2023)
- [Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions](https://mlsanity.com/paper/2304.14466/) (2023)
- [Adaptive Feature Fusion: Enhancing Generalization in Deep Learning Models](https://mlsanity.com/paper/2304.03290/) (2023)
- [Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation](https://mlsanity.com/paper/2208.10779/) (2022)
- [Vehicle Detection Performance in Nordic Region](https://mlsanity.com/paper/2403.15017/) (2024)

## Abstract (arXiv)

Aside from common challenges in remote sensing like small, sparse targets and computation cost limitations, detecting vehicles from UAV images in the Nordic regions faces strong visibility challenges and domain shifts caused by diverse levels of snow coverage. Although annotated data are expensive, unannotated data is cheaper to obtain by simply flying the drones. In this work, we proposed a sideload-CL-adaptation framework that enables the use of unannotated data to improve vehicle detection using lightweight models. Specifically, we propose to train a CNN-based representation extractor through contrastive learning on the unannotated data in the pretraining stage, and then sideload it to a frozen YOLO11n backbone in the fine-tuning stage. To find a robust sideload-CL-adaptation, we conducted extensive experiments to compare various fusion methods and granularity. Our proposed sideload-CL-adaptation model improves the detection performance by 3.8% to 9.5% in terms of mAP50 on the NVD dataset.