Research on Complementary Computer Vision Technology Based on Spectral Information Convolutional Neural Networks and Visual Transformers

With the rapid computer vision technology, it plays an important role in modern agriculture, medical diagnosis, environmental monitoring and other fields. However, the computer vision technology mainly relies on RGB image information and ignores the spectral information of objects, which may lead to incomplete information or inaccurate recognition in some applications. Spectral sensors are capable of capturing spectral information of target objects, which provide richer data support for computer vision tasks; meanwhile, Convolutional Neural Networks and vision transformers have achieved remarkable results in image feature extraction and global information capture, and this paper proposes a research idea on complementary computer vision techniques based on spectral sensors, Convolutional Neural Networks and vision transformers, aiming to improve the performance and efficiency of computer vision tasks by combining the spectral information capture capability of spectral sensors with the advantages of Convolutional Neural Networks and vision transformers in image feature extraction and global information capture.