# DAWN: Vehicle Detection in Adverse Weather Nature Dataset

arXiv: https://arxiv.org/abs/2008.05402 · HTML: https://mlsanity.com/paper/2008.05402/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 30 of 41)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Improving robustness against common corruptions with frequency biased models](https://mlsanity.com/paper/2103.16241/) (2021)
- [WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models](https://mlsanity.com/paper/2305.07528/) (2023)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies](https://mlsanity.com/paper/2401.12888/) (2024)
- [Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration](https://mlsanity.com/paper/2407.03636/) (2024)
- [Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions](https://mlsanity.com/paper/2306.13290/) (2023)
- [MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection](https://mlsanity.com/paper/2407.16448/) (2024)
- [Open-Vocabulary Object Detection via Language Hierarchy](https://mlsanity.com/paper/2410.20371/) (2024)
- [RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems](https://mlsanity.com/paper/2204.01719/) (2022)
- [ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing](https://mlsanity.com/paper/2411.02799/) (2024)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes](https://mlsanity.com/paper/2506.09557/) (2025)
- [4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions](https://mlsanity.com/paper/2301.01147/) (2022)
- [Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion](https://mlsanity.com/paper/2305.17318/) (2023)
- [Identifying Systematic Errors in Object Detectors with the SCROD Pipeline](https://mlsanity.com/paper/2309.13489/) (2023)
- [FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation](https://mlsanity.com/paper/2509.01107/) (2025)
- [In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehicles](https://mlsanity.com/paper/2204.01062/) (2022)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space](https://mlsanity.com/paper/2307.13995/) (2023)
- [Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather](https://mlsanity.com/paper/2406.00219/) (2024)
- [Rethinking Image Histogram Matching for Image Classification](https://mlsanity.com/paper/2506.01346/) (2025)
- [Robustness of Object Detection of Autonomous Vehicles in Adverse Weather Conditions](https://mlsanity.com/paper/2602.12902/) (2026)
- [WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression](https://mlsanity.com/paper/2604.06623/) (2026)
- [Safety-Critical Camera Reliability Monitoring for ADAS via Degradation-Aware Uncertainty Pattern Analysis](https://mlsanity.com/paper/2605.05439/) (2026)
- [CADENet: Condition-Adaptive Asynchronous Dual-Stream Enhancement Network for Adverse Weather Perception in Autonomous Driving](https://mlsanity.com/paper/2605.19837/) (2026)
- [Robust Onion: Peeling Open Vocab Object Detectors Under Noise](https://mlsanity.com/paper/2606.26734/) (2026)
- [Dataset for Robust and Accurate Leading Vehicle Velocity Recognition](https://mlsanity.com/paper/2204.12717/) (2022)
- [t-RAIN: Robust generalization under weather-aliasing label shift attacks](https://mlsanity.com/paper/2305.08302/) (2023)
- [DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions](https://mlsanity.com/paper/2309.08152/) (2023)

## Abstract (arXiv)

Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems. Adverse weather conditions such as heavy fog, rain, snow, and sandstorms are considered dangerous restrictions of the functionality of cameras impacting seriously the performance of adopted computer vision algorithms for scene understanding (i.e., vehicle detection, tracking, and recognition in traffic scenes). For example, reflection coming from rain flow and ice over roads could cause massive detection errors which will affect the performance of intelligent visual traffic systems. Additionally, scene understanding and vehicle detection algorithms are mostly evaluated using datasets contain certain types of synthetic images plus a few real-world images. Thus, it is uncertain how these algorithms would perform on unclear images acquired in the wild and how the progress of these algorithms is standardized in the field. To this end, we present a new dataset (benchmark) consisting of real-world images collected under various adverse weather conditions called DAWN. This dataset emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.