Pavement distress detection using convolutional neural networks with images captured via UAV
Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality. The collected images were processed and annotated for model training. Three state-of-the-art object-detection algorithms—Faster R-CNN, YOLOv3, and YOLOv4, were used to train the dataset, and their prediction performances were compared. A pavement image dataset was established with six types of distress. YOLOv3 demonstrated the best performance of the three algorithms, with a mean average precision (MAP) of 56.6%. The findings of this study assist in the inspection of non-destructive automatic pavement conditions.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
-
Supplemental Notes:
- © 2021 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
-
Authors:
- Zhu, Junqing
- Zhong, Jingtao
- Ma, Tao
- Huang, Xiaoming
- Zhang, Weiguang
- Zhou, Yang
- Publication Date: 2022-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
-
Serial:
- Automation in Construction
- Volume: 133
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0926-5805
- Serial URL: http://www.sciencedirect.com/science/journal/09265805
Subject/Index Terms
- TRT Terms: Detection and identification systems; Drones; Neural networks; Pavement distress
- Subject Areas: Highways; Maintenance and Preservation; Pavements;
Filing Info
- Accession Number: 01785396
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 25 2021 9:16AM