This paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-TSP and Coop-Frontier perform better than the baseline solution. Part-TSP has the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information.