  In this study, we address the key challenges concerning the accuracy and
effectiveness of depth estimation for endoscopic imaging, with a particular
emphasis on real-time inference and the impact of light reflections. We propose
a novel lightweight solution named EndoDepthL that integrates Convolutional
Neural Networks (CNN) and Transformers to predict multi-scale depth maps. Our
approach includes optimizing the network architecture, incorporating
multi-scale dilated convolution, and a multi-channel attention mechanism. We
also introduce a statistical confidence boundary mask to minimize the impact of
reflective areas. To better evaluate the performance of monocular depth
estimation in endoscopic imaging, we propose a novel complexity evaluation
metric that considers network parameter size, floating-point operations, and
inference frames per second. We comprehensively evaluate our proposed method
and compare it with existing baseline solutions. The results demonstrate that
EndoDepthL ensures depth estimation accuracy with a lightweight structure.
