@inproceedings{abdelmalak_pothole_2024,
	title = {Pothole {Detection} in {Asphalt} {Roads}: {A} {Comprehensive} {Approach} for {Enhanced} {Road} {Maintenance} and {Safety} with {AlexNet} {Model}},
	shorttitle = {Pothole {Detection} in {Asphalt} {Roads}},
	url = {https://ieeexplore.ieee.org/document/10620566},
	doi = {10.1109/ITC-Egypt61547.2024.10620566},
	abstract = {The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15\% accuracy, 91.38 \% sensitivity (TPR), and a surprisingly high F-score, which reached 96.52\%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.},
	urldate = {2024-08-17},
	booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Abdelmalak, Mark Emad Sobhi and Khodadadi, Nima and Zaki, Ahmed Mohamed and Eid, Marwa M. and Rizk, Faris H. and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Abualigah, Laith and El-kenawy, El-Sayed M.},
	month = jul,
	year = {2024},
	keywords = {Pothole detection, Computer vision, Computational modeling, Analytical models, Adaptation models, AlexNet, Asphalt, Asphalt road safety, Hardware, Road maintenance, Roads},
	pages = {269--274},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\CG6D6EXW\\10620566.html:text/html},
}

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The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.","urldate":"2024-08-17","booktitle":"2024 International Telecommunications Conference (ITC-Egypt)","author":[{"propositions":[],"lastnames":["Abdelmalak"],"firstnames":["Mark","Emad","Sobhi"],"suffixes":[]},{"propositions":[],"lastnames":["Khodadadi"],"firstnames":["Nima"],"suffixes":[]},{"propositions":[],"lastnames":["Zaki"],"firstnames":["Ahmed","Mohamed"],"suffixes":[]},{"propositions":[],"lastnames":["Eid"],"firstnames":["Marwa","M."],"suffixes":[]},{"propositions":[],"lastnames":["Rizk"],"firstnames":["Faris","H."],"suffixes":[]},{"propositions":[],"lastnames":["Ibrahim"],"firstnames":["Abdelhameed"],"suffixes":[]},{"propositions":[],"lastnames":["Abdelhamid"],"firstnames":["Abdelaziz","A."],"suffixes":[]},{"propositions":[],"lastnames":["Abualigah"],"firstnames":["Laith"],"suffixes":[]},{"propositions":[],"lastnames":["El-kenawy"],"firstnames":["El-Sayed","M."],"suffixes":[]}],"month":"July","year":"2024","keywords":"Pothole detection, Computer vision, Computational modeling, Analytical models, Adaptation models, AlexNet, Asphalt, Asphalt road safety, Hardware, Road maintenance, Roads","pages":"269–274","file":"IEEE Xplore Abstract Record:C\\:\\\\Users\\\\Ahmed\\\\Zotero\\\\storage\\\\CG6D6EXW\\\\10620566.html:text/html","bibtex":"@inproceedings{abdelmalak_pothole_2024,\n\ttitle = {Pothole {Detection} in {Asphalt} {Roads}: {A} {Comprehensive} {Approach} for {Enhanced} {Road} {Maintenance} and {Safety} with {AlexNet} {Model}},\n\tshorttitle = {Pothole {Detection} in {Asphalt} {Roads}},\n\turl = {https://ieeexplore.ieee.org/document/10620566},\n\tdoi = {10.1109/ITC-Egypt61547.2024.10620566},\n\tabstract = {The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15\\% accuracy, 91.38 \\% sensitivity (TPR), and a surprisingly high F-score, which reached 96.52\\%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.},\n\turldate = {2024-08-17},\n\tbooktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},\n\tauthor = {Abdelmalak, Mark Emad Sobhi and Khodadadi, Nima and Zaki, Ahmed Mohamed and Eid, Marwa M. and Rizk, Faris H. and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Abualigah, Laith and El-kenawy, El-Sayed M.},\n\tmonth = jul,\n\tyear = {2024},\n\tkeywords = {Pothole detection, Computer vision, Computational modeling, Analytical models, Adaptation models, AlexNet, Asphalt, Asphalt road safety, Hardware, Road maintenance, Roads},\n\tpages = {269--274},\n\tfile = {IEEE Xplore Abstract Record:C\\:\\\\Users\\\\Ahmed\\\\Zotero\\\\storage\\\\CG6D6EXW\\\\10620566.html:text/html},\n}\n\n","author_short":["Abdelmalak, M. E. S.","Khodadadi, N.","Zaki, A. M.","Eid, M. M.","Rizk, F. H.","Ibrahim, A.","Abdelhamid, A. A.","Abualigah, L.","El-kenawy, E. 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