Civil engineering disasters mostly occur in mountainous areas, and it is difficult to comprehensively monitor them using traditional technology, while this drawback can be avoided by utilizing UAV inclined photogrammetry technology. In this paper, with the support of the relevant experimental equipment, we obtain the images of civil engineering disasters with the help of this technology, and in order to avoid the influence of the interference factors in the images on the research results, we propose to use the K-means algorithm to pre-process the images. After completing the image processing, the improved YOLOV4 target detection algorithm is used to complete the design of the intelligent detection model of civil engineering disasters, and the processed images are input into the model for iterative training, so as to realize the intelligent management and early warning of civil engineering disasters. A region in Yunnan Province is taken as an example to explore and analyze the example. As of 2022, it is found that 180 landslides actually appeared in the region, while the model detected 172 landslides, resulting in the model’s civil engineering disaster detection accuracy of 95.56%, which is within the permissible range, proving that the model has a good application efficiency, and can provide certain help and innovative guidance for the relevant units of civil engineering disaster management.