A Smartphone-Based Computer Vision Assistance System with Neural Network Depth Estimation for the Visually Impaired

We propose a smartphone-based computer vision system for visually impaired people that uses a neural network to classify objects and estimate image depth to improve spatial orientation in the environment. For this purpose, we have developed and implemented a spatial orientation algorithm with a recursive function for calculating the sum of image array values to estimate depth. The advantage of this algorithm is the lowcomplexity of calculations, which ensures its high performance in real-time. Our system is designed to be easy to use, portable, and affordable, making it accessible to a wide range of users. The proposed system utilizes a smartphone camera and computer vision algorithms to analyze the user's environment and provide real-time feedback through audio and haptic feedback. The neural network depth estimation model is trained on a large dataset of images and corresponding depthmaps, which allows it to accurately avoid various objects in the user's field of view.