Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning

Syed Ibrahim Hassan, Dympna O’sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan

2022

Abstract

Regular pavement inspections are key to good road maintenance and detecting road defects. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of simple defects (e.g. ruts) using 3D lasers. However, such systems still require manual involvement to complete the detection of more complex pavement defects (e.g. patches). This paper proposes an automatic patch detection system using object detection techniques. To our knowledge, this is the first time state-of-the-art object detection models (Faster RCNN, and SSD MobileNet-V2) have been used to detect patches inside images acquired by 3D profiling sensors. Results show that the object detection model can successfully detect patches inside such images and suggest that our proposed approach could be integrated into the existing pavement inspection systems. The contribution of this paper are (1) an automatic pavement patch detection model for images acquired by 3D profiling sensors and (2) comparative analysis of RCNN, and SSD MobileNet-V2 models for automatic patch detection.

Paper Citation

in Harvard Style

Hassan S., O’sullivan D., Mckeever S., Power D., Mcgowan R. and Feighan K. (2022). Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 413-420. DOI: 10.5220/0010830000003124

in Bibtex Style

@conference{visapp22,
author={Syed Ibrahim Hassan and Dympna O’sullivan and Susan Mckeever and David Power and Ray Mcgowan and Kieran Feighan},
title={Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={413-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010830000003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning
SN - 978-989-758-555-5
AU - Hassan S.
AU - O’sullivan D.
AU - Mckeever S.
AU - Power D.
AU - Mcgowan R.
AU - Feighan K.
PY - 2022
SP - 413
EP - 420
DO - 10.5220/0010830000003124
PB - SciTePress