Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Intelligent Road Condition Monitoring using 3D In-Air SONAR Sensing

Amber Cassimon, Robin Kerstens, Walter Daems, Jan Steckel

Abstract

In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material classification and Road damage detection and classification. While such tasks can be performed with other sensor modalities, such as camera sensors and LiDAR sensors, these sensor modalities tend to fail in harsh sensing conditions, such as heavy rain, smoke or fog. By using a sensing modality that is robust to such interference, we enable the creation of opportunistic sensing applications, where vehicles performing other tasks (garbage collection, mail delivery, etc.) can also be used to monitor the condition of the road. For these tasks, we use a single dataset, in which different types of damages are annotated, with labels including the material of the road surface. In the material classification task, we differentiate between three different road materials: Asphalt, Concrete and Element roads. In the damage detection and classification task, we determine if there is damage, and what type of damage (independent of material type), without localizing the damage. We are succesful in determining the road surface type from SONAR sensor data, with F1 scores approaching 90% on the test set, but find that for the detection of damages performace lags, with F1 score around 75%. From this, we conclude that SONAR sensing is a promising modality to include in opportunistic sensing-based pavement management systems, but that further research is needed to reach the desired accuracy.

Intelligent Road Condition Monitoring using 3D In-Air SONAR Sensing

Abstract

In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material classification and Road damage detection and classification. While such tasks can be performed with other sensor modalities, such as camera sensors and LiDAR sensors, these sensor modalities tend to fail in harsh sensing conditions, such as heavy rain, smoke or fog. By using a sensing modality that is robust to such interference, we enable the creation of opportunistic sensing applications, where vehicles performing other tasks (garbage collection, mail delivery, etc.) can also be used to monitor the condition of the road. For these tasks, we use a single dataset, in which different types of damages are annotated, with labels including the material of the road surface. In the material classification task, we differentiate between three different road materials: Asphalt, Concrete and Element roads. In the damage detection and classification task, we determine if there is damage, and what type of damage (independent of material type), without localizing the damage. We are succesful in determining the road surface type from SONAR sensor data, with F1 scores approaching 90% on the test set, but find that for the detection of damages performace lags, with F1 score around 75%. From this, we conclude that SONAR sensing is a promising modality to include in opportunistic sensing-based pavement management systems, but that further research is needed to reach the desired accuracy.

Paper Structure

This paper contains 32 sections, 2 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Road Damage Detection
  4. Road Material Identification
  5. Methodology
  6. Dataset
  7. Preprocessing
  8. Vector preprocessing pipeline
  9. Image preprocessing pipeline
  10. Models
  11. CNN
  12. Experiments
  13. Data collection parameters
  14. Preprocessing parameters
  15. Vector preprocessing pipeline
  16. ...and 17 more sections

Figures (9)

  • Figure 1: An image showing the mounting of the eRTIS SONAR sensor (bottom left of the sensorbox) on a vehicle. The sensorbox is mounted on the back of the vehicle, looking behind.
  • Figure 2: A diagram showing how the SONAR sensor was mounted to the vehicle. The range of elevation angles is indicated in red, and the range of azimuth angles in blue.
  • Figure 3: The size of each dataset, for each fold in the dataset.
  • Figure 4: The distribution among different damages in the dataset. Note the logarithmic X-axis.
  • Figure 5: The distribution among different materials in the dataset. Note the logarithmic X-axis.
  • ...and 4 more figures