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精确高效目标检测的需求展开。我们在室内导航辅助场景下评估了四种实时目标检测算法：YOLO、SSD、Faster R-CNN 和 Mask R-CNN。通过采用室内目标检测数据集，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，为实时辅助导航场景中的算法优选提供了重要依据。本工作推动了自适应机器学习应用的发展，有助于提升视障群体的室内导航解决方案并促进无障碍环境建设。