Visually Impaired Person Assistance Based on Tensor FlowLite Technology

The most exciting thing about computer visualization is to detect a Real time object application system. This is abundantly used in many areas. With the more increase of development of deep learning such as self-driving cars, robots, safety tracking, and guiding visually impaired people, many algorithms have improved to find the relationship between video analysis and images analysis. Entire algorithms behave uniquely in the network architecture, and they have the same goal of detecting numerous objects in a composite image. It is very important to use our technology to train visually impaired people whenever they need them, as they are visually impaired and limit the movement of people in unknown places. This paper offers an application system that will identify all the possible day-to-day objects of our surroundings, and on the other side, it promotes speech feedback to the person about the sudden as well as far objects around them. This project was advanced using two different algorithms: Yolo and Yolo-v3, tested to the same criteria to measure its accuracy and performance. The SSD_MobileNet model is used in Yolo Tensor Flow and the Darknet model is used in Yolo_v3. Speech feedback: A Python library incorporated to convert statements to speech-to-speech. Both algorithms are analyzed using a web camera in a variety of circumstances to measure the correctness of the algorithm in every aspect.

@article{B2022,
  title     = {Visually Impaired Person Assistance Based on Tensor FlowLite Technology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nethravathi B and Srinivasa H P and Hithesh Kumar P and Amulya S and Bhoomika S and Banashree S Dalawai and Chakshu Manjunath},
  doi       = {10.14569/IJACSA.2022.0131171},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131171}
}