02749nas a2200289   4500000000100000000000100001008004100002260001200043653001800055653001800073653002000091653003100111100002400142700001900166700003000185700002000215700002400235700003100259700002100290700001500311245006600326856005800392300001000450490000600460520197900466022001402445       2022                            d  c12/202210aDeep Learning10aFog Computing10aImage Defogging10aMulti-Class Classification1 aZainab Hussein Arif1 aMoamin Mahmoud1 aKarrar Hameed Abdulkareem1 aSeifedine Kadry1 aMazin Abed Mohammed1 aMohammed Nasser Al-Mhiqani1 aAlaa S. Al-Waisy1 aJan Nedoma00aAdaptive Deep Learning Detection Model for Multi-Foggy Images  uhttps://www.ijimai.org/journal/bibcite/reference/3223  a26-370 v73 aThe fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications.  a1989-1660