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Abstract

To address the challenges of manual intervention in road crack detection and the inaccuracy of traditional recognition algorithms, this paper proposes a road crack recognition method based on the YOLO v3 deep learning algorithm. The dataset images were first resized to 416×416 pixels. Labelme was then used to annotate cracks in the images and convert the boundary box location information. Finally, the YOLO v3 algorithm framework was employed for model training. The results show that the YOLO v3 algorithm achieves precision, recall, and F1 scores above 95%, with an image detection speed of 0.123 seconds per image. The YOLO v3 deep learning algorithm meets the requirements for real-time road crack detection in terms of both accuracy and speed.

Publication Date

5-11-2023

DOI

10.14048/j.issn.1671-2579.2023.02.010

First Page

58

Last Page

63

Submission Date

March 2025

Reference

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