<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Road defects like potholes have a major impact on road safety and comfort. Detecting these defects manually is a highly time consuming and expensive task. Previous approaches to detect road events automatically using acceleration sensors and gyro meters showed good results. However, these results could be significantly improved with additional usage of image analysis. A large, labeled image data set is required for training and validation. This paper presents a method to automate parts of the labeling task. The method is based on a simple two step approach: at first, an unsupervised algorithm detects possible events based on the acceleration data and filters those video sequences with defects. Second, a human operator decides based on the short video sequences if the event was due to an existing road defect and labels the corresponding area in an image.</p>
</abstract>

Road defects like potholes have a major impact on road safety and comfort. Detecting these defects manually is a highly time consuming and expensive task. Previous approaches to detect road events automatically using acceleration sensors and gyro meters showed good results. However, these results could be significantly improved with additional usage of image analysis. A large, labeled image data set is required for training and validation. This paper presents a method to automate parts of the labeling task. The method is based on a simple two step approach: at first, an unsupervised algorithm detects possible events based on the acceleration data and filters those video sequences with defects. Second, a human operator decides based on the short video sequences if the event was due to an existing road defect and labels the corresponding area in an image.

A novel approach to label road defects in video data: semi-automated video analysis

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Road defects like potholes have a major impact on road safety and comfort. Detecting these defects manually is a highly time consuming and expensive task....

A novel approach to label road defects in video data: semi-automated video analysis

Article Category: Research-Article

Online veröffentlicht: 30. Apr. 2020

Seitenbereich: 1 - 9

Eingereicht: 05. Dez. 2019

DOI: https://doi.org/10.21307/ijssis-2020-007

Schlüsselwörter
Pothole detection, Road defect detection, Image labeling, Acceleration data processing, Sensor fusion, Intelligent sensor systems

© 2020 Jakob Thumm et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A novel approach to label road defects in video data: semi-automated video analysis

Article Category: Research-Article

Online veröffentlicht: 30. Apr. 2020

Seitenbereich: 1 - 9

Eingereicht: 05. Dez. 2019

DOI: https://doi.org/10.21307/ijssis-2020-007

SchlüsselwörterPothole detection, Road defect detection, Image labeling, Acceleration data processing, Sensor fusion, Intelligent sensor systems

© 2020 Jakob Thumm et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Pothole detection, Road defect detection, Image labeling, Acceleration data processing, Sensor fusion, Intelligent sensor systems