期刊论文

Kan, JM

英文

Applied Sciences-Basel

2076-3417

2023

13

17

9924-

10.3390/app13179924

S.C.: writing—original draft, methodology, visualization, investigation, validation and formal analysis. M.T.: methodology, writing—review and editing, conceptualization, supervision and funding acquisition. R.D. and J.K.: supervision, methodology and writing—review and editing. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China (Grant number 32071680); the Science and Technology Fund of Henan Province (Grant number 222102110189); Research and Innovation Initiatives of WHPU; and research funding from Wuhan Polytechnic University (Grant number 2023Y46). The authors gratefully acknowledge this support.

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The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self–calibrated RGB-D image semantic segmentation neural netw...