In this paper, a novel strategy for practical inspection planning in dry docks using unmanned aerial vehicles (UAVs) is presented. Planning is a fundamental prerequisite for accurate navigation and control of the UAV. The proposed method utilises the random sample consensus (RANSAC) algorithm to extract plane models from a voxel grid representation of the environment. For high-level planning, semantic knowledge of the environment is leveraged in a novel manner to exploit of structured obstacles, such as straight walls and orthogonal corners. In order to deal with lower-level navigation, the approach incorporates a simple graph-based local replanner to generate paths that avoid obstacles in the environment. The proposed method is compared with state-of-the-art graph-based planner in simulation and subsequently evaluated in a real environment. The paper maintains the use case of UAV vessel inspection and presents exhaustive simulation and field testing, which demonstrate the viability of the proposed approach in a fully working large-scale industrial environment.