A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline

• URL: http://arxiv.org/abs/2605.12608v1
• Date: Tue, 12 May 2026 18:01:00 GMT
• Title: A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
• Abstract summary: Object detection in adverse weather is critical for the safety of autonomous vehicles.<n>We propose Clear2Fog, an end-to-end, physics-based pipeline that simulates fog on clear-weather datasets.
• Score: 13.221137493760699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection in adverse weather is critical for the safety of autonomous vehicles; however, the scarcity of labelled, real-world foggy data remains a significant bottleneck. In this paper, we propose Clear2Fog (C2F), an end-to-end, physics-based pipeline that simulates fog on clear-weather datasets while ensuring sensor-level consistency across camera and LiDAR. By using monocular depth estimation and a novel atmospheric light estimation method, C2F overcomes structural artifacts and chromatic biases common in existing techniques. A human perceptual study confirms C2F's physical realism, with the generated images being preferred 92.95% of the time over an established method. Utilising a training set of 270,000 images from the Waymo Open Dataset, we conduct an extensive data efficiency study to investigate how environmental diversity influences model robustness. Our findings reveal that models trained on mixed-density fog datasets at 75% scale outperform those trained on fixed-density datasets at 100% scale. Furthermore, we investigate the sim-to-real transfer by fine-tuning pre-trained models on real-world foggy data. We demonstrate that a tenfold increase over the default fine-tuning learning rate successfully overcomes negative transfer from synthetic biases, resulting in a 1.67 mAP improvement over real-only baselines. The C2F pipeline provides a scalable framework for enhancing the reliability of autonomous systems in adverse weather and demonstrates the potential of diverse synthetic datasets for efficient model training.

Related papers

• Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification [54.952203312050564]
  Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification.<n>We propose FogFool, a physically plausible adversarial framework that generates fog-based perturbations.<n>Experiments on two benchmark RS datasets demonstrate that FogFool achieves superior performance.
  arXiv  Detail & Related papers  (2026-04-16T05:47:57Z)
• Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach [7.735941375698213]
  Existing methods struggle to capture the complex interactions between LiDAR signals and atmospheric phenomena, leading to unrealistic representations.<n>This paper presents a physics-informed learning framework for generating realistic LiDAR data under adverse weather conditions.
  arXiv  Detail & Related papers  (2026-04-01T06:36:59Z)
• FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection [17.442807911285772]
  Foreign Object Debris (FOD) within aircraft fuel tanks presents critical safety hazards.<n>There is a notable lack of dedicated datasets for the complex, enclosed environments found inside fuel tanks.<n>We present a novel dataset, FOD-S2R, composed of real and synthetic images of the FOD within a simulated aircraft fuel tank.
  arXiv  Detail & Related papers  (2025-12-01T06:16:26Z)
• Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix [1.15692661299731]
  Enhancing robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology.<n>This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to generate realistic datasets with weather-based augmentations.
  arXiv  Detail & Related papers  (2025-05-13T05:12:07Z)
• Pseudo-Label Guided Real-World Image De-weathering: A Learning Framework with Imperfect Supervision [57.5699142476311]
  We propose a unified solution for real-world image de-weathering with non-ideal supervision.<n>Our method exhibits significant advantages when trained on imperfectly aligned de-weathering datasets.
  arXiv  Detail & Related papers  (2025-04-14T07:24:03Z)
• Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring [0.0]
  We create a physics-based synthetic image and data generator, resulting in a machine learning model that achieves comparable precision (0.86), recall (0.63), F1 scores (0.71), and engineering property predictions (R2=0.82) Our study demonstrates that synthetic data can eliminate human reliance in ML and provides a means for domain awareness in cases where many feature detections per image are needed.
  arXiv  Detail & Related papers  (2024-08-02T20:15:15Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation [151.70234052015948]
  We propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7%.
  arXiv  Detail & Related papers  (2022-11-20T15:49:11Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.