  With the rapid development of drone technology, the application of drones
equipped with visual sensors for building inspection and surveillance has
attracted much attention. View planning aims to find a set of near-optimal
viewpoints for vision-related tasks to achieve the vision coverage goal. This
paper proposes a new clustering-based two-step computational method using
spectral clustering, local potential field method, and hyper-heuristic
algorithm to find near-optimal views to cover the target building surface. In
the first step, the proposed method generates candidate viewpoints based on
spectral clustering and corrects the positions of candidate viewpoints based on
our newly proposed local potential field method. In the second step, the
optimization problem is converted into a Set Covering Problem (SCP), and the
optimal viewpoint subset is solved using our proposed hyper-heuristic
algorithm. Experimental results show that the proposed method is able to obtain
better solutions with fewer viewpoints and higher coverage.
