Jabeen Koo  Yujin Kim  Bokyung Amy Kwon  Kyungtae Kang

10.5626/JOK.2026.53.3.230

Object Detection multi-stage preprocessing Hyperparameter Optimization foggy

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.