Street Crossing Aid Using Light-weight CNNs for the Visually Impaired

TL;DR

This paper presents a lightweight deep learning mobile application that assists visually impaired individuals in crossing streets safely by providing real-time traffic light and direction information with high accuracy and efficiency.

Contribution

It introduces LytNetV2, a novel lightweight CNN model based on MobileNetV3 principles, optimized for real-time street crossing aid on mobile devices, outperforming existing methods.

Findings

Achieves 96% classification accuracy and 6.15° average angle error.

Runs at 16.34 frames per second on iOS devices.

Received positive feedback from visually impaired users.

Abstract

In this paper, we address an issue that the visually impaired commonly face while crossing intersections and propose a solution that takes form as a mobile application. The application utilizes a deep learning convolutional neural network model, LytNetV2, to output necessary information that the visually impaired may lack when without human companions or guide-dogs. A prototype of the application runs on iOS devices of versions 11 or above. It is designed for comprehensiveness, concision, accuracy, and computational efficiency through delivering the two most important pieces of information, pedestrian traffic light color and direction, required to cross the road in real-time. Furthermore, it is specifically aimed to support those facing financial burden as the solution takes the form of a free mobile application. Through the modification and utilization of key principles in MobileNetV3 such as depthwise seperable convolutions and squeeze-excite layers, the deep neural network model achieves a classification accuracy of 96% and average angle error of 6.15 degrees, while running at a frame rate of 16.34 frames per second. Additionally, the model is trained as an image classifier, allowing for a faster and more accurate model. The network is able to outperform other methods such as object detection and non-deep learning algorithms in both accuracy and thoroughness. The information is delivered through both auditory signals and vibrations, and it has been tested on seven visually impaired and has received above satisfactory responses.