Detection and classification of asphalt pavement cracks using YOLOv5

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Hosseinzadeh Hassan; Ghiami Bajgirani Ali; Delavarian Mohadeseh

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Modeling in Engineering Year:2023 | Volume:21 | Issue:72 Page(s): 175-193

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Detection and classification of asphalt pavement cracks using YOLOv5

Author(s)

Hosseinzadeh Hassan | Ghiami Bajgirani Ali | Delavarian Mohadeseh | Issue Writer Certificate

Keywords

Pavement management

Crack

machine learning

Deep learning

YOLOv5

Abstract

Automatic pavement Crack detection is essential for assessing road maintenance and ensuring safe driving. Traditional Crack detection has problems such as low efficiency and lack of complete detection. This study aims to solving the problems of traditional Crack detection methods and using Deep learning models. We proposed a method based on object detection algorithms for pavement Crack detection and discussed the latest YOLOv5 series models for pavement Crack detection while explaining the theoretical concepts. Finally, a Crack detection model and effective Pavement management is presented. The proposed model can determine the type, position and geometric characteristics of Cracks accurately and at a higher speed in comparison with other methods. For this purpose, the images that had been taken from the asphalt of Mashhad roads were used to train and evaluate the model. Images were labeled for both linear Crack and surface Crack. Proposed model is developed using five YOLOv5 series algorithms and transfer learning and were evaluated for accuracy and speed of prediction. The models’ accuracy is between 77 to 98% and the prediction speed is between 17.4 to 105 milliseconds, which indicates the optimal performance of the models. The v5s model with 92.8% accuracy and a speed of 23.9 ms is selected as the final model for real prediction of Cracks in one of the main thoroughfares of Mashhad. Based on the dimensions and the type of predicted Crack and the use of the proposed decision tree, the maintenance approach for each part was determined.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans