This study addresses the need for accurate and efficient object detection in assistive technologies for visually impaired individuals. We evaluate four real-time object detection algorithms YOLO, SSD, Faster R-CNN, and Mask R-CNN within the context of indoor navigation assistance. Using the Indoor Objects Detection dataset, we analyze detection accuracy, processing speed, and adaptability to indoor environments. Our findings highlight the trade-offs between precision and efficiency, offering insights into selecting optimal algorithms for realtime assistive navigation. This research advances adaptive machine learning applications, enhancing indoor navigation solutions for the visually impaired and promoting accessibility.

翻译：本研究针对视障人士辅助技术中对准确高效物体检测的需求，评估了YOLO、SSD、Faster R-CNN和Mask R-CNN四种实时物体检测算法在室内导航辅助场景下的表现。通过使用室内物体检测数据集，我们系统分析了检测精度、处理速度以及对室内环境的适应性。研究结果揭示了精度与效率之间的权衡关系，为实时辅助导航系统选择最优算法提供了理论依据。本工作推动了自适应机器学习应用的发展，提升了视障群体的室内导航解决方案，促进了无障碍环境的建设。