Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and...

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Bibliographic Details
Published in:Journal of advanced transportation 2020, Vol.2020 (2020), p.1-15
Main Authors: Wu, Difei, Liu, Chenglong, Weng, Zihang, Du, Yuchuan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Asphalt pavements
Automation
Cable television broadcasting industry
Cameras
Cracks
Deep learning
Defects
Euclidean geometry
Feature extraction
Global positioning systems
GPS
Image detection
Mapping
Matching
Methods
Pavements
Reproduction (copying)
Roads & highways
Satellite navigation systems
Stitching
Structural hierarchy
Support vector machines
Transportation
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Summary:Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/5804835