Files

Download

Download Full Text (922 KB)

Publisher

Technological University Dublin

Description

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.

Publication Date

2023

Keywords

object detection, pavement inspection systems, road maintenance, deep learning

Disciplines

Computer Sciences

Conference

First Annual Teaching and Research Showcase 2023

DOI

https://doi.org/10.21427/5QFE-1973

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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


COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.