Khan, M.A.-M.; Harseno, R.W.; Kee, S.-H.; Nahid, A.-A. Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System. Remote Sens.2023, 15, 3573.
Khan, M.A.-M.; Harseno, R.W.; Kee, S.-H.; Nahid, A.-A. Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System. Remote Sens. 2023, 15, 3573.
Khan, M.A.-M.; Harseno, R.W.; Kee, S.-H.; Nahid, A.-A. Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System. Remote Sens.2023, 15, 3573.
Khan, M.A.-M.; Harseno, R.W.; Kee, S.-H.; Nahid, A.-A. Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System. Remote Sens. 2023, 15, 3573.
Abstract
Crack inspection is important to monitor the structural health of pavement structures
and to facilitate an easier rehabilitation process. Currently, pavement crack inspection is conducted
manually, which is inefficient and costly at the same time. For solving the problem, this work has
developed a robotic system for automated data collection and analysis in real-time. The robotic
system navigates on the pavement and collects visual images from the surface. A deep learning-based
semantic segmentation framework named RCDNet was proposed and implemented on the onboard
computer of the robot to identify cracks from the visual images. Simulation results show that the deep
learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was
tested on both indoor and outdoor environments and was observed that it can complete inspecting a
3m × 2m grid within 10 minutes and a 2.5m × 1m grid within 6 minutes. This outcome shows that
the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a
severity map based on the results from visual images was also generated to provide an idea of which
locations should be paid more attention to repair in a test grid.
Keywords
crack detection; deep learning; mobile robotic system; NDE analysis; pavement inspection
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
