Abstract
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.
Highlights
Various types of damage may occur on road surfaces owing to external factors—such as the weather, overloaded vehicles, and traffic volume—and internal factors—such as defects in materials and cross-sections
Smartphones provide an optimal platform for testing a road-surface anomaly detection method that employs both a deep neural networks (DNNs) inference model to identify road-surface anomalies and acceleration data acquisition because they are equipped with built-in processors, LTE communication modules, three-axis accelerometers, gyroscopes, and cameras
A system was developed to identify road-surface anomalies in collected images using an fully convolutional neural network (FCN) model while simultaneously processing three-axis accelerations collected during the concurrent period of time
Summary
Various types of damage may occur on road surfaces owing to external factors—such as the weather, overloaded vehicles, and traffic volume—and internal factors—such as defects in materials and cross-sections. Smartphones provide an optimal platform for testing a road-surface anomaly detection method that employs both a DNN inference model to identify road-surface anomalies and acceleration data acquisition because they are equipped with built-in processors, LTE communication modules, three-axis accelerometers, gyroscopes, and cameras. In recent years, both image-recognition- and vibration-based detection methods have been researched using smartphones, but few studies have attempted to combine these two different methods. Collection of Road-Surface Anomaly Information Using a Smartphone Camera and Accelerometer
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