Abstract
Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.
Highlights
Highways are a strategic resource in a country and they provide the basis of social development [1]
We explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in
According to the classification criterion described in the Technical Specifications for Maintenance of Highway Asphalt Pavement [38] and the classification feasibility with the acquired point cloud with an interval of 5 cm, the classification system established in this study focuses on extracting severe pavement distresses (Table 3)
Summary
Highways are a strategic resource in a country and they provide the basis of social development [1]. Many evaluation indexes (such as the pavement condition index, pavement roughness index, riding quality index, and pavement structure strength index) have been used to measure pavement quality and usage status [8,9] To obtain these indexes, it is imperative to adopt reliable approaches [10]. Traditional methods utilize measurements that are taken in situ along with visual examinations and interpretations [11] These methods can collect detailed pavement surface condition data for various types of distress and provide accurate and valuable information. These methods have some disadvantages, such as being time consuming [7], expensive [12], labor-intensive [13], and subjective [14]. Recent studies [17,18,19,20] have demonstrated a growing interest in remote sensing applications for pavement monitoring and management
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