Smart Guide for Pedestrian Traffic Light Status Identification

This paper develops a low-cost electronic visual assistant for visually impaired people, which determines the status of pedestrian traffic lights using artificial intelligence. By means of audio notifications, it describes the status of traffic lights, assisting blind people when moving around the streets. The computer vision and artificial intelligence software runs locally, avoiding the problem of latency in connectivity and is hosted on a Raspberry pi 4 development board. The transfer learning technique is used to obtain a reliable and adaptable convolutional neural network model for the problem of image identification. Employing conducting field tests on all the components of the prototype and its computer vision system, its effectiveness is verified. Through statistical analysis, detection results of 83.3% were obtained for red pedestrian traffic lights and an average of 77.7% in three out of the four categories for green traffic light detection. Additionally, a 100% error-free classification rate was determined, demonstrating its effectiveness and reliability.