@Article{sdhm.2025.063887,
AUTHOR = {Tao Jin, Siqi Gu, Zhekun Shou, Hong Shi, Min Zhang},
TITLE = {Investigation of Attention Mechanism-Enhanced Method for the Detection of Pavement Cracks},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {19},
YEAR = {2025},
NUMBER = {4},
PAGES = {903--918},
URL = {http://www.techscience.com/sdhm/v19n4/62795},
ISSN = {1930-2991},
ABSTRACT = {The traditional You Only Look Once (YOLO) series network models often fail to extract satisfactory features for road detection, due to the limited number of defect images in the dataset. Additionally, most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features. To address these issues, this study collected a large number of original road surface images using road detection vehicles. A large-capacity crack dataset was then constructed, with various shapes of cracks categorized as either cracks or fractures. To improve the training performance of the YOLOv5 algorithm, which showed unsatisfactory results on the original dataset, this study used median filtering to preprocess the crack images. The preprocessed images were combined to form the training set. Moreover, the Coordinate Attention (CA) attention module was integrated to further enhance the model’s training performance. The final detection model achieved a recognition accuracy of 88.9% and a recall rate of 86.1% for detecting cracks. These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.},
DOI = {10.32604/sdhm.2025.063887}
}