Abstract:
Safe and independent mobility is one of the major daily challenges faced by the visually impaired. To navigate a new area safely, they need to know the location of obstacles and other things in their path. They struggle with object detection and obstacle avoidance, making it challenging to navigate new or unfamiliar situations and be aware of obstacles and their relative positions. However, establishing secure and safe mobility and pathfinding for the visually impaired is a critical issue in their life that must be solved accurately and efficiently. Recognizing currency is another severe problem for them because different notes in our country have similar colors, surfaces and sizes causing major problems for the visually impaired. Object recognition alone may not be sufficient to assist visually impaired individuals effectively. Incorporating lateral position identification can provide users with a sense of spatial orientation within their environment, enabling them to navigate paths toward recognized objects more accurately. In this thesis, a system is proposed to assist visually impaired individuals by identifying both navigation objects and their corresponding lateral positions, and recognizing Bangladeshi currency. This system aims to serve as a comprehensive walking guide, offering benefits for both indoor and outdoor navigation. By enhancing spatial awareness and providing critical information about their surroundings, this system will enable visually impaired users to make informed decisions regarding their movements and actions; thereby, improving their independence and quality of life. The system utilizes EfficientNet, a Convolutional Neural Network architecture for Machine Learning to design a model for currency and navigation objects that can recognize fifteen classes of objects for assisting the visually impaired. The architecture also applies computational logic to the model to identify lateral positions of the navigation objects; and thus conceptualizing a system that can act as a walking guide for visually impaired people aiding navigation and awareness. The model proposed in the system has been evaluated with real-world objects to assess the performance of the proposed method. The experimental analysis demonstrates that the system achieves notable Accuracy, Precision, Recall, and F1 scores, highlighting its prospective relevance and effectiveness in the field.