A Context-Aware Doorway Alignment System with Depth Estimation for Intelligent Wheelchair Navigation

Navigating through narrow spaces and doorways can be a daily struggle for wheelchair users, often resulting in frustration, collisions, or reliance on external assistance. These challenges highlight a pressing need for intelligent, user-centered mobility solutions that go beyond traditional object detection. In this study, we propose a lightweight segmentation model that integrates context-attention and geometric reasoning to support real-time doorway alignment. The model incorporates a convolutional block attention Module (CBAM) for refined feature emphasis, a content-guided convolutional attention fusion module (CGCAFusion) for multi-scale semantic integration, an unsupervised depth estimation module, and an alignment estimation module that provides intuitive navigational guidance. Trained on the DeepDoorsv2 dataset, our model demonstrates a mean average precision (mAP50) of 95.8% and a F1 score of 93% while maintaining hardware efficiency with 2.96 M parameters, outperforming baseline models. By eliminating the need for depth sensors and enabling contextual decision-making, this study offers a robust solution to improve indoor mobility and delivering actionable feedback to support safe and independent navigation for wheelchair users.