Vision Based Navigation Assistant Using Object Detection And Depth Estimation

Vision-based navigation has become increasingly important in fields such as assistive technology, robotics, and autonomous driving, as it enables systems to understand and interact with complex environments. This study introduces a Vision-Based Navigation Assistant that combines object detection with depth estimation to improve real-time awareness and navigation safety. The system utilizes deep learning techniques to detect and categorize surrounding objects while estimating their distances through monocular or stereo vision approaches. This integrated method allows the system to deliver relevant information about obstacles, pathways, and potential risks. Designed for efficiency, the framework can run on embedded devices, ensuring portability and minimal processing delay. Furthermore, it provides feedback through audio or haptic signals, making it especially useful for visually impaired individuals and autonomous systems. Experimental evaluations indicate enhanced accuracy in identifying objects and estimating distances, resulting in dependable performance across both indoor and outdoor settings. Overall, the proposed solution demonstrates the effectiveness of computer vision in developing intelligent navigation aids that enhance mobility, safety, and user independence.