Robot-assisted airway intubation application needs high accuracy in locating
targets and organs. Two vital landmarks, nostrils and glottis, can be detected
during the intubation to accommodate the stages of nasal intubation. Automated
landmark detection can provide accurate localization and quantitative
evaluation. The Detection Transformer (DeTR) leads object detectors to a new
paradigm with long-range dependence. However, current DeTR requires long
iterations to converge, and does not perform well in detecting small objects.
This paper proposes a transformer-based landmark detection solution with
deformable DeTR and the semantic-aligned-matching module for detecting
landmarks in robot-assisted intubation. The semantics aligner can effectively
align the semantics of object queries and image features in the same embedding
space using the most discriminative features. To evaluate the performance of
our solution, we utilize a publicly accessible glottis dataset and
automatically annotate a nostril detection dataset. The experimental results
demonstrate our competitive performance in detection accuracy. Our code is
publicly accessible.

本文提出了一种基于变形 DeTR 和语义对齐匹配模块的基于 Transformer 的标志检测解决方案，用于检测机器辅助气道插管中的标志。实验结果表明我们的解决方案在检测准确性方面具有竞争力。