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Community structure is one of the most important features of complex networks. Modularity-based methods for community detection typically rely on heuristic algorithms to optimize a specific community quality function. Such methods are limited by two major defects: (1) the resolution limit problem, which prohibits communities of heterogeneous sizes being simultaneously detected, and (2) divergent outputs of the heuristic algorithm, which make it difficult to differentiate relevant and irrelevant results. In this paper, we propose an improved method for community detection based on a scalable community "fitness function." We introduced a new parameter to enhance its scalability, and a strict strategy to filter the outputs. Due to the scalability, on the one hand our method is free of the resolution limit problem and performs excellently on large heterogeneous networks, while on the other hand it is capable of detecting more levels of communities than previous methods in deep hierarchical networks. Moreover, our strict strategy automatically removes redundant and irrelevant results, without any artificial selection. As a result, our method neatly outputs only the stable and unique communities, which are largely interpretable by the a priori knowledge about the network, including the implanted structures within synthetic networks, or metadata for real-world networks.