Next Article in Journal
Combining 10 Matrix Pressure Sensor to Read Human Body’s Pressure in Sleeping Position in Relation with Decubitus Patients
Next Article in Special Issue
A Machine Learning Approach to Solve the Network Overload Problem Caused by IoT Devices Spatially Tracked Indoors
Previous Article in Journal
Utilizing B-Spline Curves and Neural Networks for Vehicle Trajectory Prediction in an Inverse Reinforcement Learning Framework
Previous Article in Special Issue
Machine Learning Enabled Food Contamination Detection Using RFID and Internet of Things System
Article

A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting

1
Department of Mechatronics Engineering, Universidad Católica Boliviana “San Pablo”, La Paz 4807, Bolivia
2
Electronic Engineering and Computer Science School, Queen Mary University of London, London E1 4FZ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Lei Shu
J. Sens. Actuator Netw. 2022, 11(1), 15; https://doi.org/10.3390/jsan11010015
Received: 6 January 2022 / Revised: 2 February 2022 / Accepted: 9 February 2022 / Published: 14 February 2022
(This article belongs to the Special Issue Machine Learning in IoT Networking and Communications)
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. View Full-Text
Keywords: road damage detection; computer vision; deep learning; smart maintenance road damage detection; computer vision; deep learning; smart maintenance
Show Figures

Figure 1

MDPI and ACS Style

Salcedo, E.; Jaber, M.; Requena Carrión, J. A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting. J. Sens. Actuator Netw. 2022, 11, 15. https://doi.org/10.3390/jsan11010015

AMA Style

Salcedo E, Jaber M, Requena Carrión J. A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting. Journal of Sensor and Actuator Networks. 2022; 11(1):15. https://doi.org/10.3390/jsan11010015

Chicago/Turabian Style

Salcedo, Edwin, Mona Jaber, and Jesús Requena Carrión. 2022. "A Novel Road Maintenance Prioritisation System Based on Computer Vision and Crowdsourced Reporting" Journal of Sensor and Actuator Networks 11, no. 1: 15. https://doi.org/10.3390/jsan11010015

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop