To address the issues of high computational complexity and insufficient real-time performance encountered in three-dimensional object detection for complex aircraft maintenance scenes, a three-dimensional object detection method which integrates visual camera and LiDAR data and based on prior information is proposed. First, the parameters of a camera and LiDAR are calibrated, the point cloud obtained by LiDAR is preprocessed to obtain an effective three-dimensional point cloud, and the YOLOv7 algorithm is used to identify aircraft fuselage targets in the camera images. Next, the depth of the target object is calculated based on its prior length using the Efficient Perspective-n-Point (EPnP) method. Finally, depth information and point cloud clustering methods are utilized to complete three-dimensional object detection and identify obstacles. Experimental results show that the proposed method can accurately detect targets from environmental point clouds, with a recognition accuracy of 94.70%. Furthermore, the processing time for one frame is 42.96 ms, which indicates good performance in terms of both recognition accuracy and real-time capability, thus satisfying the collision risk detection requirements during aircraft movement.