MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention

Aruco markers are crucial for navigation in complex indoor environments, especially for those with visual impairments. Traditional CNNs handle image segmentation well, but transformers excel at capturing long-range dependencies, essential for machine vision tasks. Our study introduces MoNetViT (Mini-MobileNet MobileViT), a lightweight model combining CNNs and MobileViT in a dual-path encoder to optimize global and spatial image details. This design reduces complexity and boosts segmentation performance. The addition of a multi-query attention (MQA) module enhances multi-scale feature integration, allowing end-to-end learning guided by ground truth. Experiments show MoNetViT outperforms other semantic segmentation algorithms in efficiency and effectiveness, particularly in detecting Aruco markers, making it a promising tool to improve navigation aids for the visually impaired.