  We present HetNet (Multi-level \textbf{Het}erogeneous \textbf{Net}work), a
highly efficient mirror detection network. Current mirror detection methods
focus more on performance than efficiency, limiting the real-time applications
(such as drones). Their lack of efficiency is aroused by the common design of
adopting homogeneous modules at different levels, which ignores the difference
between different levels of features. In contrast, HetNet detects potential
mirror regions initially through low-level understandings (\textit{e.g.},
intensity contrasts) and then combines with high-level understandings
(contextual discontinuity for instance) to finalize the predictions. To perform
accurate yet efficient mirror detection, HetNet follows an effective
architecture that obtains specific information at different stages to detect
mirrors. We further propose a multi-orientation intensity-based contrasted
module (MIC) and a reflection semantic logical module (RSL), equipped on
HetNet, to predict potential mirror regions by low-level understandings and
analyze semantic logic in scenarios by high-level understandings, respectively.
Compared to the state-of-the-art method, HetNet runs 664$\%$ faster and draws
an average performance gain of 8.9$\%$ on MAE, 3.1$\%$ on IoU, and 2.0$\%$ on
F-measure on two mirror detection benchmarks.
