EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer

• URL: http://arxiv.org/abs/2308.02716v2
• Date: Wed, 16 Aug 2023 17:39:15 GMT
• Title: EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer
• Authors: Yangke Li
• Abstract summary: We propose a novel lightweight solution named EndoDepthL that integrates 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. To better evaluate the performance of monocular depth estimation in endoscopic imaging, we propose a novel complexity evaluation metric.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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