An Indoor Object Detection and Navigation System

Abstract

“Indoor Object Detection for Blind” is a dedicated assistive technology project designed to enhance indoor navigation for visually impaired individuals. Utilizing advanced computer vision techniques, the system detects and identifies objects in real-time using wearable devices like smart glasses or smartphones. It integrates YOLO-based deep learning models, fine-tuned with a custom indoor dataset, to ensure high accuracy in recognizing objects such as furniture, doors, and appliances. Additionally, natural language processing (NLP) enables context-aware descriptions, providing adaptive feedback. The project is designed to be flexible and seamlessly integrate with wearable technology, ensuring a smooth and efficient user experience. The system evaluates various aspects of objects, including their spatial positioning and contextual information, to assist users in real-time. By concentrating solely on object detection within indoor environments, this system significantly improves spatial awareness and navigation for visually impaired individuals. Extensive testing demonstrates the system's performance in detecting and identifying objects while minimizing false positives.