In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage.