@article{M28811931,
title = "Enhanced Object Detection in Fog Using a Multi-Stage Preprocessing Ensemble with Hyperparameter Optimization in YOLOv8",
journal = "Journal of KIISE, JOK",
year = "2026",
issn = "2383-630X",
doi = "10.5626/JOK.2026.53.3.230",
author = "Jabeen Koo, Yujin Kim, Bokyung Amy Kwon, Kyungtae Kang",
keywords = "object detection, multi-stage preprocessing, hyperparameter optimization, foggy",
abstract = "Conventional deep learning-based object detection models often struggle with detection performance in foggy environments. To improve this performance, in specific environments, research has focused on image correction and blending techniques. However, most single-processing methods have drawbacks, such as over-brightening certain areas or darkening edges. In this study, we propose a multi-processing model designed to enhance object detection performance in foggy conditions using continuous hyperparameter optimization based on the YOLOv8 model. We aim to address limitations of individual techniques by combining multiple preprocessing methods, specifically optimizing hyperparameters for gamma correction and histogram matching. Our performance evaluation results indicate that the ensemble model we developed outperformed individual single processing models on various metrics, suggesting that it can significantly enhance object detection performance in challenging environments like fog."
}