Cracking

as one of the most common defects in concrete dams

weakens the integrity and durability of dam structures; crack detection has been a crucial task in the operation and maintenance management of concrete dams. Aimed at the drawbacks of traditional image-processing techniques in crack detection－such as substantial manual intervention and limited generalization ability

this paper presents a semantic segmentation model of dam cracks that incorporates multi-scale features and attention mechanisms. This model uses ResNet-50 as its backbone network for integrating the Path Aggregation Network to recycle shallow features

and makes use of the mechanisms of channel attention and spatial attention. These mechanisms enhance the model's ability to identify critical features

thus effectively improving its segmentation accuracy. Then

based on its semantic segmentation results

the digital image technology is adopted to quantify the geometric characteristics of cracks

including area

length

average width

and maximum width. Tests on a crack image dataset show this new model achieves a crack segmentation Intersection over Union of 82.02% and an F1 score of 90.12%; Quantification results of geometric characteristics exhibit an excellent agreement with the real values and a satisfactory accuracy. Thus

our method demonstrates significant potential for application in crack detection and geometric characteristics quantification for concrete dams.