Automated Dam Crack Detection Using YOLOv10 and UAV Imagery for Structural Health Monitoring

Abstract Dams are critical components of national infrastructure, and the presence of undetected surface cracks can lead to severe safety hazards. Conventional inspection methods are often time-consuming, limited by human error, and difficult to implement on a large scale, making timely assessments a persistent challenge. To overcome these limitations, this study presents an automated crack detection framework that leverages YOLOv10 in conjunction with UAV-captured imagery. The speed and accuracy of YOLOv10, combined with the UAV’s ability to access hard-to-reach areas, significantly enhance the efficiency and reliability of the inspection process. The proposed system achieved promising results, recording a precision of 0.93, a recall of 0.81, and an F1-score of 0.86— reflecting its capability to rapidly and accurately detect surface-level cracks. This approach represents a step forward in advancing intelligent infrastructure management by facilitating early maintenance and contributing to safer and more sustainable dam operations, particularly through its potential integration with digital twin technologies.