Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network

ABSTRACT

Crack detection on roads’ surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it.

KEYWORDS:

• Pavement cracks
• deep learning
• CNN
• orthoimage
• UAV
• pavement management system

Acknowledgments

The authors are grateful to Dr. Masood Varshosaz, Dr. Mohammad Saadatseresht, and Mr. Ali Mahdinezhad Gargari for their assistance with UAV imaging and data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.