Identification of markers in challenging conditions for people with visual impairment using convolutional neural network

Abstract

People with visual impairment face a lot of difficulties in their daily activities. Several researches have been conducted to find smart solutions using mobile devices to help people with visual impairment perform tasks. This paper focuses on using assistive technology to help people with visual impairment in indoor navigation using markers. The essential steps of a typical navigation system are identifying the current location, finding the shortest path to the destination, and navigating safely to the destination using navigation feedback. In this research, the authors proposed a system to help people with visual impairment in indoor navigation using markers. In this system, the authors have re-defined the identification step to a classification problem and used convolutional neural networks to identify markers. The main contributions of this paper are: (1) A system to help people with visual impairment in indoor navigation using markers. (2) Comparing QR codes with Aruco markers to prove that Aruco markers work better. (3) Convolutional neural network has been implemented and simplified to detect the candidate markers in challenging conditions and improve response time. (4) Comparing the proposed model with another model to prove that it gives better accuracy for training and testing.