UI overlap is a phenomenon where one UI component visually covers another. While this overlap is necessary to construct rich visual hierarchies, it is also a root cause of usability issues and performance bottlenecks. However, a systematic, data-driven understanding of its prevalence and patterns has been lacking. To bridge this gap, we conduct the first large-scale empirical study on UI overlap in the OpenHarmony ecosystem. We analyze 100 popular apps, classifying 33,262,624 overlap instances through a novel three-tiered taxonomy. Our findings reveal that high-cost occlusion is a critical and previously hard-to-detect performance defect where resource-intensive components are rendered while visually obscured. We propose HCO-Eye, an innovative tool that leverages multimodal vision-language models (VLMs) to automatically detect such issues, successfully identifying 34 high-cost occlusion cases in commercial apps. Our study not only provides the first comprehensive understanding of UI overlap in OpenHarmony but also offers a practical tool to automatically diagnose complex performance-related UI bugs. Our tools are publicly available.