Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Korea.
Sensors (Basel). 2021 Jan 14;21(2):561. doi: 10.3390/s21020561.
Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.
路面应保持良好状态，以确保驾驶者的安全。为此，存在各种路面监测系统，每种系统都有其特定的优点和缺点。在这项研究中，开发了一种基于智能手机的双采集方法系统，该系统能够采集路面异常的图像并在检测到异常时测量车辆的加速度，以探索两种不同方法的互补优势。进行了一次道路测试，共采集了 1896 个路面图像和相应的三轴加速度数据。所有图像均根据异常的存在和类型进行分类，并比较分析了重力方向上加速度最大变化的直方图。当不考虑异常类型时，很难通过直方图识别它们的影响。当考虑车辆车轮是否驶过异常以及排除引起加速度较小变化的纵向异常时，直方图之间的差异变得明显。尽管本研究中使用的基于图像的监测系统本身性能不佳，但通过重力方向上加速度最大变化的特定范围，可以准确推断路面异常的严重程度。
