Abstract
The maintenance of critical infrastructure is a costly necessity that developing countries often struggle to deliver timely repairs. The transport system acts as the arteries of any economy in development, and the formation of potholes on roads can lead to injuries and the loss of lives. Recently, several countries have enabled pothole reporting platforms for their citizens, so that repair work data can be centralised and visible for everyone. Nevertheless, many of these platforms have been interrupted because of the rapid growth of requests made by users. Not only have these platforms failed to filter duplicate or fake reports, but they have also failed to classify their severity, albeit that this information would be key in prioritising repair work and improving the safety of roads. In this work, we aimed to develop a prioritisation system that combines deep learning models and traditional computer vision techniques to automate the analysis of road irregularities reported by citizens. The system consists of three main components. First, we propose a processing pipeline that segments road sections of repair requests with a UNet-based model that integrates a pretrained Resnet34 as the encoder. Second, we assessed the performance of two object detection architectures—EfficientDet and YOLOv5—in the task of road damage localisation and classification. Two public datasets, the Indian Driving Dataset (IDD) and the Road Damage Detection Dataset (RDD2020), were preprocessed and augmented to train and evaluate our segmentation and damage detection models. Third, we applied feature extraction and feature matching to find possible duplicated reports. The combination of these three approaches allowed us to cluster reports according to their location and severity using clustering techniques. The results showed that this approach is a promising direction for authorities to leverage limited road maintenance resources in an impactful and effective way.
Highlights
Roads are an essential factor in the economic and social development of any country
In contrast to many real-time road damage detection proposals, we focused on finding accurate detection methods for offline automated image analysis; We propose a combined supervised and unsupervised approach for request clustering according to their location
We evaluated each model considering their Average Precision (AP) per class, which can be calculated by finding the area under the precision–recall curve [53]: AP =
Summary
The investment in new road infrastructure always results in new opportunities for services and asset interchange between the connected populations. Similar to any type of infrastructure, roads can be damaged over time, due to a number of factors including weather conditions, traffic action (starting and stopping), and moisture infiltration. The Ministry of Transport and Highways in India declared that 2015 people lost their lives in 2018 due to pothole-related accidents [1]. This figure corresponds to an average of more than five daily deaths, which according to the same source keeps growing year by year
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
