Adaptive deep learning detection model for multi-foggy images

• Zainab Hussein Arif ^[1] ; Moamin A. Mahmoud ^[1] ; Karrar Hameed Abdulkareem ^[5] ; Seifedine Kadry ^[6] ; Mazin Abed Mohammed ^[2] ; Mohammed Nasser Al-Mhiqani ^[3] ; Alaa S. Al-Waisy ^[7] ; Jan Nedoma ^[4]
  1. [1] Universiti Tenaga Nasional

     Universiti Tenaga Nasional

     Malasia

     
  2. [2] University of Anbar

     University of Anbar

     Irak

     
  3. [3] Keele University

     Keele University

     Newcastle-under-Lyme District, Reino Unido

     
  4. [4] University of Ostrava

     University of Ostrava

     Chequia

     
  5. [5] Al-Muthanna University
  6. [6] Norrof University College
  7. [7] Imam Ja’afar Al-Sadiq University

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• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 7, Nº. 7, 2022, págs. 26-37
• Idioma: inglés
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• Resumen

  • The 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.