AI-Driven Object and Scene Recognition System for Blind Assistance

The rapid advancement of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has enabled the development of intelligent systems capable of perceiving and interpreting real-world environments. This project presents an Intelligent Visual Assistant that integrates an ESP32-CAM module with a server based AI object detection framework to provide real-time visual understanding and voice-based feedback. The ESP32-CAM captures images and transmits them wirelessly to a Flask-based server, where the images are processed using the YOLOv8 object detection algorithm to identify objects present in the scene. The detected information is converted into audible speech using a Text-to-Speech (TTS) engine and delivered to the user through a speaker or Bluetooth-enabled device. The proposed system adopts a hybrid edge–server architecture that balances low-cost hardware with high-performance AI processing. Experimental results demonstrate reliable object detection accuracy with low response latency under local network conditions, making the system suitable for real-time applications. The solution is particularly beneficial for assistive technologies for visually impaired individuals, smart surveillance etc.