A Survey on Smart Assistive System for Visually Impaired People Obstruction Avoidance through Object Detection and Classification

Object detection is becoming a crucial part of many modern technologies, including intelligent surveillance to driverless cars. In this study, we investigate its function in developing an intelligent assistive framework for people with visual impairments. The technology combines audio synthesis, multilingual translation, and image captioning to give contextual scene narration and real-time blockage detection. A webcam captures environmental visuals, which are processed using the BLIP Transformer for scene description and the YOLOv8 deep learning model for object and traffic sign recognition. Multilingual audio feedback is generated using gTTS, while a voice-controlled interface ensures hands-free interaction. To improve efficiency, a cool-down mechanism prevents repetitive alerts. This integrated strategy improves safety and freedom for visually impaired users by providing semantic comprehension and language-inclusive narration, in contrast to previous approaches that only addressed obstacle location