Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer

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.

Road Hazard Assessment Using Pothole and Traffic Data in South Korea

“…e algorithm used here is basically referred from previous studies. Pothole data can be detected using the technological system illustrated in Fig- ure 1. e accuracy of this technology has been verified in previous reviews [13,14,18]. Using DAISS, point-based detected pothole data can be transformed into a roadway section-based map using the Korean standard transportation node-link system (https://its.go.kr/nodelink/intro).…”

“…Furthermore, there is a great deal of flexibility regarding the equipment and price of adopting these technologies. e technology employed in this research therefore collects pothole data using in-vehicle smartphone accelerometers and cameras to obtain images of road surface anomalies [13,14]. ese images are then analyzed with an image recognition-based fully convolutional neural network model to determine if they contain potholes.…”

A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams

“…It was found that a classifier pretrained on such sensor data allows for localizing potholes, major cracks, joints, and manholes but also speed bumps [ 48 , 49 ]. On the other hand, it was reported that the smartphone sensor-based approach would hardly be used to reliably detect relatively minor surface defects such as longitudinal cracks [ 50 ].…”

“…The collected data sequences were segmented and labeled manually by the authors, based on recorded images of the corresponding routes. After consultations with road maintenance specialists and taking into account results of the previous studies [ 48 , 49 , 50 ], it was decided to limit the inspection to four road surface conditions with class labels assigned as follows: “flat road” (Class 0), “pothole” (Class 1), “speed bump” (Class 2), and “bumpy road” (Class 3).…”

Sensing Advancement and Health Monitoring of Transport Structures

“…Lee et al [9] present a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the vehicle' s three-axis acceleration upon their detection. Images are classified based on type and scale of anomalies and histograms of maximum variations of the acceleration in the gravitational direction are compared.…”