A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management

• URL: http://arxiv.org/abs/2202.13285v1
• Date: Sun, 27 Feb 2022 04:08:00 GMT
• Title: A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management
• Authors: Philippe Heitzmann
• Abstract summary: We propose a supervised object detection approach to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera. Our results rank in the top 5 of 121 teams that entered the IEEE's 2020 Global Road Damage Detection ("GRDC") Challenge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the Institute of Electrical and Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated image dataset of various road distresses calling upon academic and other researchers to submit innovative deep learning-based solutions to these road hazard detection problems. Making use of this dataset, we propose a supervised object detection approach leveraging You Only Look Once ("YOLO") and the Faster R-CNN frameworks to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score experimental results ranking in the top 5 of 121 teams that entered this challenge as of December 2021.

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