Real Time Object Detection with Auditory Alerts For Visually Impaired Individuals

Abstract

This paper presents a real-time object detection system designed to assist visually impaired individuals through audio alerts. The system is implemented as a web-based application using HTML, CSS, and JavaScript, making it accessible and easy to use. It utilizes TensorFlow.js along with the COCO-SSD pre-trained model to detect objects from live video captured through a camera. The system identifies common objects such as people, vehicles, and everyday items, and determines their position relative to the user. Based on the detection results, appropriate alert messages are generated and converted into speech using the Web Speech API. This enables users to receive real-time information about their surroundings without relying on visual input. The system operates continuously and provides quick responses with minimal delay. Overall, the proposed solution is cost-effective, efficient, and demonstrates the practical application of machine learning in assistive technology. The system is designed to function in real-world environments and can handle multiple object detections simultaneously. Its web-based nature ensures portability and ease of access across devices. This approach highlights the potential of integrating modern web technologies with artificial intelligence for developing practical assistive solutions. The system supports real-time processing and can detect multiple objects simultaneously. It is designed to be user-friendly and accessible across different devices through a web browser. This approach demonstrates how machine learning and web technologies can be effectively combined to create a practical assistive solution.