Automatic road crack recognition based on deep learning networks from UAV imagery

F. Samadzadegan, F. Dadrass javan, M. Hasanlou, M. Gholamshahi, F. Ashtari mahini

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)
5 Downloads (Pure)

Abstract

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition.
Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
EditorsM.R. Delavar, R. Ali Abbaspour, S. Farzaneh
Place of PublicationTehran
PublisherCopernicus
Pages685-690
VolumeX-4/W1-2022
DOIs
Publication statusPublished - 14 Jan 2023
EventGeoSpatial Conference 2022 - virtual, Tehran, Iran, Islamic Republic of
Duration: 19 Feb 202322 Feb 2023
https://www.sru.ac.ir/en/4860-2/

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

ConferenceGeoSpatial Conference 2022
Country/TerritoryIran, Islamic Republic of
CityTehran
Period19/02/2322/02/23
OtherJoint 6th SMPR and 4th GIResearch Conferences
Internet address

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