Degree
Bachelor of Science (Computer Science)
Department
Department of Computer Science
School
School of Mathematics and Computer Science (SMCS)
Advisor
Dr. Imran Khan, Assistant Professor, Institute of Business Administration, Karachi
Co-Advisor
Shalin Amir Ali, Folio3
Keywords
Custom Heuristics, Computer Vision, Mobile Application, SDK Integration, Assistive Technology
Abstract
This report presents the design and development of a smartphone-based navigation system aimed at assisting blind users in independently traversing the IBA University campus. The system integrates two core modules: (1) a Navigation Module that provides turn-by-turn guidance using OpenStreetMap (OSM) data and GPS positioning, and (2) an Obstacle Detection Module powered by a lightweight YOLOv8n model running on-device via TensorFlow Lite to detect hazards such as pedestrians, vehicles, and bicycles. Real-time audio feedback is provided through Google Text-to-Speech, enabling users to receive navigation instructions and obstacle alerts without relying on external hardware. The Android application was evaluated through simulated campus scenarios, focusing on usability, alert accuracy, and responsiveness. Results from these simulations indicate that the system can effectively guide users and provide timely obstacle warnings in a typical campus environment. Future improvements include real-world testing with blind individuals, enhanced obstacle detection, and the integration of indoor navigation support.
Tools and Technologies Used
Platforms & Tools:
- Android Studio
- OsmAnd & OpenStreetMap (OSM) SDK
Programming Language:
- Kotlin
Libraries & Models:
- YOLOv8n (for real-time object and obstacle detection)
- Google Translate (for voice navigation cues)
Methodology
The development approach follows a use-case driven methodology, where each user flow is identified, built, and tested in iterative cycles. Group members divided responsibilities based on modules and use cases.
This allowed for:
- Targeted optimization of each feature
- Manual testing in real-world scenarios
- Refinement through iterative feedback and improvements
Special emphasis was placed on lightweight computation, avoiding processing-heavy models and implementing custom heuristics for depth estimation to ensure the app works effectively on standard Android devices.
Document Type
Restricted Access
Submission Type
BSCS Final Year Project
Recommended Citation
Khan, Z. I., Ejaz, F., Khurram, A., & Ahmed, U. (2025). Blind Navigation System. Retrieved from https://ir.iba.edu.pk/fyp-bscs/24
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