关键词: 水库岸坡；裂缝识别；数字图像；自动检测；数据融合；地质灾害；乌东德水库

Abstract: The rapid development of digital image technology has led to its widespread application in the field of automatic crack recognition. However, the accuracy of crack recognition using a single algorithm model in complex backgrounds still needs improvement. To address this, we used digital orthophoto maps (DOMs) obtained through UAV oblique photography as the data source for automatic crack identification research. Firstly, considering the imaging characteristics of different crack types, three algorithm models (threshold segmentation, edge detection, and supervised classification) were employed for preliminary crack identification, using image pixel gradient, gray scale value, and RGB value as indicators. Next, the initial extraction results were processed by morphological restoration and two filtering algorithms of direction and length to handle background noise. Finally, a minimum-risk Bayesian probability model fusion method was proposed to integrate the recognition results of different models at the decision level. The engineering application results demonstrated that: the three algorithm models effectively extracted cracks on reservoir bank slopes, each achieving an accuracy rate of over 70%. Model fusion minimized the omission of true cracks, with the final fused model achieving a recognition accuracy of 95.4%. The fusion algorithm successfully distinguished target cracks from background information in reservoir slope crack identification, significantly reducing image noise and improving recognition precision.

Key words: reservoir bank slope; cracks recognition; digital image; automatic detection; data fusion; geological disaster; Wudongde Reservoir