Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients
It addresses a domain-specific problem for visually impaired individuals by providing a practical indoor assistance solution, though it appears incremental as it combines existing methods (YOLO NAS Small and Super Gradients) for a known bottleneck.
This paper tackles the problem of small object detection for indoor assistance to the blind by proposing a technique using YOLO NAS Small architecture optimized with Super Gradients, achieving real-time detection with low latency and high accuracy for objects like furniture and appliances.
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.