TY  - JOUR
T1  - Enhanced Object Detection in Fog Using a Multi-Stage Preprocessing Ensemble with Hyperparameter Optimization in YOLOv8
AU  - Koo, Jabeen 
AU  - Kim, Yujin 
AU  - Kwon, Bokyung Amy 
AU  - Kang, Kyungtae 
JO  - Journal of KIISE, JOK
PY  - 2026
DA  - 2026/1/14
DO  - 10.5626/JOK.2026.53.3.230
KW  - object detection
KW  - multi-stage preprocessing
KW  - hyperparameter optimization
KW  - foggy
AB  - 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.