Enhancing Object Detection in Assistive Technology for the Visually Impaired: A DETR-Based Approach

This paper presents a real-time obstacle detection and recognition system designed to enhance navigation for visually impaired individuals through assistive technology. The system integrates a mobile application equipped with a mini camera for real-time image capture and employs advanced deep learning techniques for object detection and classification. A comparative evaluation of YOLOv8, Faster R-CNN and DETR (Detection Transformer) is conducted based on precision, Recall, F1-score, confidence score and processing efficiency. DETR demonstrates superior performance, achieving a 99% confidence score, 98% precision and a processing speed of 40ms per frame. While faster R-CNN and YOLOv8 provide competitive results, they offer a trade-off between accuracy and computational efficiency. The system follows a structured a structured workflow, including real-time acquisition, preprocessing, innovative data augmentation and optimization for edge devices using TensorFlow Lite for efficiency deployment. It classifies 80 types of obstacles, such as pedestrians, vehicles and traffic signal and provides immediate audio feedback to ensure safe navigation. The model trained over 20 epochs achieves an accuracy of 98% in the final epoch. This study introduces a scalable and practical solution integrating IoT and real-time image processing, empowering visually impaired users with enhanced mobility and safety.