Publication:
Adaptive Deep Learning Detection Model for Multi-Foggy Images

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dc.contributor.authorArif Z.H.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAbdulkareem K.H.en_US
dc.contributor.authorKadry S.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorAl-Mhiqani M.N.en_US
dc.contributor.authorAl-Waisy A.S.en_US
dc.contributor.authorNedoma J.en_US
dc.contributor.authorid57350531200en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57197854295en_US
dc.contributor.authorid55906598300en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid57197853907en_US
dc.contributor.authorid57188925513en_US
dc.contributor.authorid57014879400en_US
dc.date.accessioned2023-05-29T09:39:02Z
dc.date.available2023-05-29T09:39:02Z
dc.date.issued2022
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 (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. � 2022, Universidad Internacional de la Rioja. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.9781/ijimai.2022.11.008
dc.identifier.epage37
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85143626658
dc.identifier.spage26
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85143626658&doi=10.9781%2fijimai.2022.11.008&partnerID=40&md5=1dc4018fa013e691d42bd7eab005b197
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27051
dc.identifier.volume7
dc.publisherUniversidad Internacional de la Riojaen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Interactive Multimedia and Artificial Intelligence
dc.titleAdaptive Deep Learning Detection Model for Multi-Foggy Imagesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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