Vision AI: A Deep Learning-Based Object Recognition System for Visually Impaired People Using TensorFlow and OpenCV
Prof. D. B. Dandekar · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Object detection is a challenging task in computer vision that can provide valuable information for visually impaired people. In this paper, we propose a deep learning-based model that detects objects for blind people using frameworks such as TensorFlow and OpenCV. Our system uses a pre-trained model built on YOLO v7 to detect and recognize objects in real-time from images or videos captured by a camera. Other versions of YOLO have also been taken into consideration. However, based on some recent comparisons, YOLOv7 is the fastest and most accurate official YOLO version. It achieves 2% higher accuracy than Cascade-Mask R-CNN models at dramatically increased inference speed (509% faster). The results are then converted to speech using text-to-speech technology and delivered to the user through headphones or a speaker. Our system is intended to be an IoT-based project that can recognize common objects and people. We evaluated our system on several datasets such as Common Object in Context (COCO)- a large-scale labelled dataset containing 1.5 million object images, to demonstrate its accuracy and efficiency. We believe that our system can offer a practical and affordable way for visually impaired people to access visual information and enhance their quality of life.