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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.
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This content will become publicly available on February 1, 2026
Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images
Pavement surface distresses are analyzed by transportation agencies to determine section performance across their pavement networks. To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. However, the precision of this novel technology often leads to inaccuracies that must be verified by pavement engineers. Developments in artificial intelligence and machine learning (AI/ML) can aid in the progress of more robust and precise detection algorithms. Deep learning models are efficient for visual distress identification of pavement. With the use of 2D/3D pavement images, surface distress analysis can help train models to efficiently detect and classify surface distresses that may be caused by traffic loading, weather, aging, and other environmental factors. The formation of these distresses is developing at a higher rate in coastal regions, where extreme weather phenomena are more frequent and intensive. This study aims to develop a YOLOv5 model with 2D/3D images collected in the states of Louisiana, Mississippi, and Texas in the U.S. to establish a library of data on pavement sections near the Gulf of Mexico. Images with a resolution of 4096 × 2048 are annotated by utilizing bounding boxes based on a class list of nine distress and non-distress objects. Along with emphasis on efforts to detect cracks in the presence of background noise on asphalt pavements, six scenarios for augmentation were made to evaluate the model’s performance based on flip probability in the horizontal and vertical directions. The YOLOv5 models are able to detect defined distresses consistently, with the highest mAP50 scores ranging from 0.437 to 0.462 throughout the training scenarios.
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- Award ID(s):
- 2213694
- PAR ID:
- 10612885
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 25
- Issue:
- 4
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 1145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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