轻量模型实时检测小物体，助盲人室内导航
Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients
- 用YOLO NAS Small+Super Gradients优化轻量模型
- 实现实时检测家具等小物体，延迟低精度高
- 适合需要实时语音引导的盲人辅助系统
视觉障碍人士的辅助技术正因目标检测算法的进步而发展。本文提出一种针对盲人室内导航的小物体检测新方法。采用YOLO NAS Small轻量高效目标检测模型，并通过Super Gradients训练框架进行优化，实现对家具、电器及日常用品等小物体的实时检测，显著提升盲人在室内环境中的空间感知与交互能力。该方法强调低延迟与高准确率，支持及时的语音提示，为视障者提供实用的室内辅助解决方案。实验验证了系统的有效性。
原文摘要 · Abstract (English)
Advancements in object detection algorithms have opened new avenues for assistive technologies that cater to the needs of visually impaired individuals. This paper presents a novel approach for indoor assistance to the blind by addressing the challenge of small object detection. We propose a technique YOLO NAS Small architecture, a lightweight and efficient object detection model, optimized using the Super Gradients training framework. This combination enables real-time detection of small objects crucial for assisting the blind in navigating indoor environments, such as furniture, appliances, and household items. Proposed method emphasizes low latency and high accuracy, enabling timely and informative voice-based guidance to enhance the user's spatial awareness and interaction with their surroundings. The paper details the implementation, experimental results, and discusses the system's effectiveness in providing a practical solution for indoor assistance to the visually impaired.
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