Beta
335665

YOLOv7 Deep Learning Model for Pavement Crack Detection Using Close Range Photogrammetry Dataset.

Article

Last updated: 24 Dec 2024

Subjects

-

Tags

Civil Engineering

Abstract

Developing an effective system for detecting and classifying pavement cracks is crucial for ensuring traffic safety. However, the procedure of manual inspection for identifying these cracks can be hazardous and time-consuming. Thus, it's essential to implement an automated approach to make the detection process more efficient. Overcoming challenges like varying intensity levels, inconsistent data availability, and ineffective traditional methods make this task complicated. This research's aim is to contribute to the development of an efficient system for detecting pavement cracks. Pavement crack detection using close range photogrammetry is a process for identifying, characterizing and evaluating pavement surface cracks that is revolutionizing the speed, accuracy and cost of assessing the structural integrity of pavements. These images are used by analysis software to generate detailed digital maps of the pavement surface. These digital maps can then be used to identify and measure pavement cracking. The use of close-range photogrammetry for pavement crack detection offers numerous advantages over traditional pavement inspection methods, including improved accuracy and flexibility in the analysis of pavement cracks and the ability to analyze large areas of pavement quickly. The quality of the images captured depends on the type of camera used, but most cameras offer high-resolution imaging at close range. The customized YOLOv7 model, which is a state-of-the-art deep learning algorithm, was used in this study. The precision of the outcome reports is 0.854 and recall from the custom dataset is 0.755. The results of the suggested system were satisfactory compared to the results of reference studies.

DOI

10.21608/pserj.2024.246447.1276

Keywords

Deep Learning YOLOv7, Pavement Crack Detection, Close range photogrammetry

Authors

First Name

Fekry

Last Name

Ashraf

MiddleName

-

Affiliation

Department of Civil Engineering, Higher Technological Institute, Tenth of Ramadan City, Egypt

Email

eng_fekry2020@yahoo.com

City

Tenth of Ramadan city

Orcid

-

First Name

mostafa

Last Name

rabah

MiddleName

-

Affiliation

civil engineering banha university

Email

m.rabah@bhit.bu.edu.eg

City

elshrouq

Orcid

-

First Name

Essam

Last Name

Ghanem

MiddleName

-

Affiliation

civil engineering banha university

Email

essam.ghanem@bhit.edu.eg

City

cairo

Orcid

-

Volume

28

Article Issue

2

Related Issue

48838

Issue Date

2024-06-01

Receive Date

2023-11-05

Publish Date

2024-06-01

Page Start

18

Page End

30

Print ISSN

1110-6603

Online ISSN

2536-9377

Link

https://pserj.journals.ekb.eg/article_335665.html

Detail API

https://pserj.journals.ekb.eg/service?article_code=335665

Order

2

Type

Original Article

Type Code

813

Publication Type

Journal

Publication Title

Port-Said Engineering Research Journal

Publication Link

https://pserj.journals.ekb.eg/

MainTitle

YOLOv7 Deep Learning Model for Pavement Crack Detection Using Close Range Photogrammetry Dataset.

Details

Type

Article

Created At

24 Dec 2024