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

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.

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

“…There are also some studies which have attempted to detect cracks in complicated scenarios, but they focused on different pavement distress types, like Ref. [34] which classified pavement distress types into nine groups, while Refs. [60,61] classified pavement distress into longitudinal cracks, transverse cracks, alligator cracks, and potholes.…”

“…Maeda et al [32,33] developed two road damage detection models based on the single shot multibox detector (SSD) algorithm and generative adversarial network (GAN), whose datasets were derived from smartphone images and artificial images, respectively. A computer vision model was proposed using the YOLOv5 algorithm for detecting and classifying pavement distress of nine classes in [34]; a sensor-based model has also been investigated in this study.…”

Vision-Based Asphalt Pavement Defect Detection Using Deep CNN with BIM Integration

“…The CNN is trained on a dataset of pavement images to identify cracks without the need for preprocessing, achieving around 96% accuracy. This automated approach offers a promising solution for efficiently and accurately detecting pavement distress, such as cracks, which is crucial for maintaining road safety and capacity [9]. The text refers to a study that utilizes computer vision technology to estimate the influence of lines for highway bridges.…”