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.

HetNet 是一种高效的镜像检测网络，通过低层次的理解（如强度对比）初步检测出潜在的镜像区域，并结合高层次的理解（如语境不连续性）进行预测，以达到准确而高效的镜像检测。