EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916122902659072 |
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| author | Li, Yangke |
| author_facet | Li, Yangke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02716 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer Li, Yangke Computer Vision and Pattern Recognition 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. |
| title | EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.02716 |