Paper Title
SMART SPECTACLES FOR REAL-TIME OBSTACLE DETECTION BY INTEGRATING MACHINE LEARNING AND IMAGE PROCESSING

Abstract
Smart spectacles equipped with advanced image processing and machine learning technologies offer a promising solution for real-time obstacle detection, enhancing both safety and navigation for users. This paper presents the integration of machine learning algorithms with image processing techniques to develop a smart spectacles system capable of identifying and alerting users to potential obstacles in their environment. The proposed system utilizes a compact, wearable camera to capture continuous visual data, which is then processed using machine learning algorithms to detect and classify obstacles with high accuracy. The integration of real-time image processing ensures immediate analysis of the visual feed, while the machine learning model continuously learns and adapts to diverse environments and obstacle types. The system provides user feedback through auditory or haptic signals, enabling timely and effective navigation assistance. Experimental results demonstrate the efficacy of the integrated approach in various real-world scenarios, showcasing significant improvements in obstacle detection accuracy and response time. Keywords - Advanced Image Processing, AI&ML, Camera, Raspberry Pi, Objection Detection.