In order to efficiently plan the flight path of the UAV in the 3D coverage detection task, a path planning model that meets the coverage requirements is established, which can be divided into two steps: The first step is to determine the viewpoint and line of sight of the UAV inspection, and the second step calculate the collision-free access sequence of viewpoints. First, starting from the 3D point cloud of the inspection target, a method of generating candidate viewpoints based on K-means clustering is proposed, and an incomplete graph model of candidate viewpoint interconnections is constructed. Secondly, a sorting-oriented hybrid ant colony algorithm ( sortingoriented hybrid ant colony algorithm, S-HACO) finds the UAV inspection path, and the optimization goal takes into account the length of the path, the number of viewpoints, the number of sharp turns, etc. The simulation results show that the viewpoint obtained by this method compared with the offset method and random sampling method, the number is reduced by 96. 25% and 42. 10%, respectively, and the performance of the S-HACO algorithm is better than that of the traditional algorithm, the objective function is reduced by 19. 14%, and the running time of the algorithm is reduced by 25. 27%. The effectiveness of the model and the feasibility of the algorithm.