对比4种检测算法，为视障者导航选最优实时方案
Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms
- 在室内场景下评估YOLO、SSD等4种实时检测算法
- 发现精度与速度存在明显权衡，YOLO表现最佳平衡性
- 适合开发无障碍导航系统的工程师和研究者参考
本研究针对视障人士辅助技术中的精准高效物体检测需求，评估了四种实时物体检测算法（YOLO、SSD、Faster R-CNN、Mask R-CNN）在室内导航辅助场景中的表现。基于Indoor Objects Detection数据集，分析了检测准确率、处理速度及对室内环境的适应能力。研究揭示了精度与效率之间的权衡关系，为实时辅助导航系统中算法的优化选择提供了依据。该工作推动了自适应机器学习在无障碍应用中的发展，提升了视障人群的室内导航体验，助力信息平权。
原文摘要 · Abstract (English)
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.
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