CNN-Based Recognition Algorithm for Four Classes of Roads

Abstract

In recent years, location-based augmented reality games have become popular globally. Consequently, the risk of collisions or accidents while walking with mobile devices has increased. Using smartphones while walking can distract pedestrians and can lead to negative consequences for traffic safety. In addition, a survey of visually impaired people revealed that they found border recognition inconvenient due to the lowered jaws between the driveway and sidewalks. In this study, an accident prevention system is proposed based on a convolutional neural network by segregating the walking environments into four classes (sidewalks, driveways, crosswalks, and braille blocks). A total of 3,200 datasets (3,000 for training and 200 for test) were used in our study. We show that the proposed system has the accuracy of 90% for validation data, and the recognition rate of 90% or above for test data.