Show simple item record

dc.contributor.authorArif, Zainab Hussein
dc.contributor.authorMahmoud, Moamin A.
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorKadry, Seifedine
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAl-Mhiqani, Mohammed Nasser
dc.contributor.authorAl-Waisy, Alaa S.
dc.contributor.authorNedoma, Jan
dc.identifier.citationInternational Journal of Interactive Multimedia and Artificial Intelligence. 2022, vol. 7, issue 7, p. 26-37.cs
dc.description.abstractThe 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 ( 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.cs
dc.publisherUNIR - Universidad Internacional de La Riojacs
dc.relation.ispartofseriesInternational Journal of Interactive Multimedia and Artificial Intelligencecs
dc.subjectdeep learningcs
dc.subjectfog detectioncs
dc.subjectfoggy imagecs
dc.subjectmulti-class classificationcs
dc.titleAdaptive deep learning detection model for multi-foggy imagescs
dc.description.sourceWeb of Sciencecs

Files in this item

This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as