Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

Mohammad Alamgeer; Hend Khalid Alkahtani; Mashael Maashi; Mahmoud Othman; Anwer Mustafa Hilal; Mohamed Ibrahim Alsaid; Azza Elneil Osman; Amani A. Alneil

doi:10.1109/ACCESS.2023.3283299

Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

Mohammad Alamgeer
, Hend Khalid Alkahtani
, Mashael Maashi
, Mahmoud Othman
, Anwer Mustafa Hilal
, Mohamed Ibrahim Alsaid
, Azza Elneil Osman
, Amani A. Alneil

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56%.

Original language	English
Pages (from-to)	61986-61994
Number of pages	9
Journal	IEEE Access
Volume	11
DOIs	https://doi.org/10.1109/ACCESS.2023.3283299
State	Published - 2023
Externally published	Yes

Keywords

computer vision
Flooding
machine learning
parameter tuning
road damage

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ACCESS.2023.3283299

Cite this

@article{0faa8c83b3f14fc6867cc2d131ab7edf,

title = "Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection",

abstract = "Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56\%.",

keywords = "computer vision, Flooding, machine learning, parameter tuning, road damage",

author = "Mohammad Alamgeer and Alkahtani, \{Hend Khalid\} and Mashael Maashi and Mahmoud Othman and Hilal, \{Anwer Mustafa\} and Alsaid, \{Mohamed Ibrahim\} and Osman, \{Azza Elneil\} and Alneil, \{Amani A.\}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2023",

doi = "10.1109/ACCESS.2023.3283299",

language = "English",

volume = "11",

pages = "61986--61994",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

AU - Alamgeer, Mohammad

AU - Alkahtani, Hend Khalid

AU - Maashi, Mashael

AU - Othman, Mahmoud

AU - Hilal, Anwer Mustafa

AU - Alsaid, Mohamed Ibrahim

AU - Osman, Azza Elneil

AU - Alneil, Amani A.

PY - 2023

Y1 - 2023

N2 - Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56%.

AB - Floods are one of the most severe and most frequent natural calamities. It causes enormous economic damage and even leads to higher mortality rates. Studies on damage detection of roads using artificial intelligence (AI) methods gained more attention currently, especially in the development of smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD technique aims to exploit the remote sensing images to classify different kinds of roads. For noise removal process, the OFWNN-RDD technique utilizes Gabor filtering (GF) technique. In addition, the OFWNN-RDD technique uses the DenseNet121 model for feature vector generation with modified barnacles mating optimization (MBMO) based hyperparameter optimizer. Finally, FWNN image classification approach is used for road damage detection. The simulation values exhibit the supremacy of the OFWNN-RDD technique over other models with improved road damage detection accuracy of 98.56%.

KW - computer vision

KW - Flooding

KW - machine learning

KW - parameter tuning

KW - road damage

UR - https://www.scopus.com/pages/publications/85161477582

U2 - 10.1109/ACCESS.2023.3283299

DO - 10.1109/ACCESS.2023.3283299

M3 - Article

AN - SCOPUS:85161477582

SN - 2169-3536

VL - 11

SP - 61986

EP - 61994

JO - IEEE Access

JF - IEEE Access

ER -

Optimal Fuzzy Wavelet Neural Network Based Road Damage Detection

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this