Abstract
Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO‐DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.
Highlights
The economic framework and evolving networks of cities depend on road traffic [1]. e detection of pavement distress is an important part of pavement maintenance
To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC)
Components of 3D Pavement Crack Detection System. e asphalt pavement crack detection system based on the FO-DPC algorithm has three main components. e flow chart in Figure 1 shows how the system collects data and detects cracks. e proposed system involves the acquisition of the 3D data of the asphalt pavement, 3D pavement data clustering based on the FO-DPC algorithm, and pavement crack feature extraction
Summary
The economic framework and evolving networks of cities depend on road traffic [1]. e detection of pavement distress is an important part of pavement maintenance. Traditional artificial crack detection methods affect traffic and have low efficiency, strong subjectivity, and low accuracy; they have been unable to meet the growing pavement maintenance needs for a long time [3]. With the development of computer technology, the emergence of many pavement crack detection technologies based on digital image processing, such as Laplace, Sobel, Prewitt, Roberts, and Canny operators and other edge detection algorithms, has greatly improved pavement crack detection efficiency and accuracy. These detection algorithms are highly sensitive to noise. There are some quite effective 2D crack detection methods [9], 2D crack detection methods are affected by light, shadow, pavement signs, and oil stains. erefore, there is growing scholarly interest in the study of 3D detection of asphalt pavement cracks [10]
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