Alam, Golam RabiulSusmit, Tahsin AshrafeeMehejabin, MalihaHasan, IsratulKausar, Azmain IbnAkbar, Suraiya Binte2025-02-132025-02-1320242024-10ID 20301088ID 20301264ID 20301072ID 20301144http://hdl.handle.net/10361/25395Cataloged from PDF version of thesis.Includes bibliographical references (pages 70-72).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.The purpose of our study is to create new technology that will provide a revolutionary navigation system with significant improvement of mobility and independence for visually impaired people. We utilize YOLOv11 and Faster R-CNN to detect an object which is used in combination with Llama 3.2–3B Instruct for context-aware navigation by providing helpful guidance of our current essential location. Our paper tackles the failure points in today’s technologies with lack of flexibility for dynamic and unfamiliar environments, unreliable performance under changes in lighting conditions and inefficient obstacle detection. By training these models together and selecting the one with the highest confidence score, we enhance spatial awareness, identifying obstacles in key areas like the left, right, or center. This approach, complemented by personalized navigation instructions, ensures improved decisionmaking and safety in real-world scenarios. Using advanced locational technologies available today and imagining those of tomorrow, we aspire to render current navigation methods obsolete by fostering more efficient, real-time and autonomous tools for visually impaired people as they become part of the familiar or unfamiliar environments. After fine-tuning the Llama 3.2-3B-Instruct model, BLEU-4 increased from 0.0442 to 0.1175, and ROUGE-L improved from 0.2102 to 0.3204, indicating enhanced text generation fluency and coherence.72 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.YOLOv11Faster R-CNNLlama 3.2-3B InstructObject detectionNavigation systemVisually impairedVisually impaired persons--Navigation.Computer vision.Object recognition--Computer algorithms.Thesis