Pavement Distress Identification Based on Computer Vision and Controller Area Network (CAN) Sensor Models

Cuthbert Ruseruka (), Judith Mwakalonge, Gurcan Comert, Saidi Siuhi, Frank Ngeni and Kristin Major
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Cuthbert Ruseruka: Department of Engineering, South Carolina State University, Orangeburg, SC 29117, USA
Judith Mwakalonge: Department of Engineering, South Carolina State University, Orangeburg, SC 29117, USA
Gurcan Comert: Computer Science, Physics, and Engineering Department, Benedict College, 1600 Harden St, Columbia, SC 29204, USA
Saidi Siuhi: Department of Engineering, South Carolina State University, Orangeburg, SC 29117, USA
Frank Ngeni: Department of Engineering, South Carolina State University, Orangeburg, SC 29117, USA
Kristin Major: Computer Science, Physics, and Engineering Department, Benedict College, 1600 Harden St, Columbia, SC 29204, USA

Sustainability, 2023, vol. 15, issue 8, 1-18

Abstract: Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments.

Keywords: pavement maintenance; XGBoost; CAN sensors in roads condition; YOLOv5; sensor-based model; pavement condition monitoring; Deep Learning models for road condition monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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