Abstract:
This thesis presents the development of a mobile app-based solution for rose color
detection designed specifically for visually impaired individuals. The solution
leverages advanced Vision Transformer (ViT) architectures, particularly ViT-B16
and ViT-B32, to enable real-time, accurate, and accessible color recognition.
Addressing the limitations of traditional color identification methods, the proposed
solution empowers users to independently experience and identify rose colors,
fostering inclusivity and autonomy. The study incorporates synthetic data generation
techniques to overcome the challenges of limited labeled datasets, enhancing model
generalization across diverse environmental conditions. The lightweight nature of
ViT-B16 and ViT-B32 ensures compatibility with standard mobile devices,
optimizing computational efficiency while maintaining high accuracy. Intuitive
feedback tailored to the needs of visually impaired users provides actionable and
descriptive insights into detected colors. The research methodology involves data
collection, model training using ViT-B16 and ViT-B32 architectures, and iterative
app design, followed by rigorous testing under varied real-world conditions to
evaluate performance. The results demonstrate the app’s effectiveness, achieving
99.81% accuracy, and potential as a practical assistive tool. This study contributes to
the growing field of AI-driven assistive technologies by addressing critical gaps in
accessibility, dataset diversity, and real-world adaptability. Beyond its immediate
application in rose color detection, the findings have broader implications for
developing inclusive technologies that enhance the quality of life for individuals with
visual impairments. The thesis concludes with recommendations for future
improvements and scalability of the proposed solution.
