Review on “Real -Time Object Detection with Speech Feedback for Visually Impaired”

Review on “Real -Time Object Detection with Speech Feedback for Visually Impaired”

Mr. M. S. Khan1, Ritul Ghumare2, Prerna Malode3, Rutuja Murkute4, Sakshi Pagare5

*1 Assistant Professor, Department of Information Technology, Matoshri College of Engineering & Research Centre, Eklahare, Maharashtra, India

*2,3,4,5 Students, Department of Information Technology, Matoshri College of Engineering & Research Centre, Eklahare, Maharashtra, India

ABSTRACT –

It’s a known fact that estimated number of blind persons in the world is about 285 million, approximately equal to the 20% of the Indian Population. They are mostly dependent on someone for even accessing their basic daily needs. In our project, we used TensorFlow, it's a new library from Google. TensorFlow model our neural networks. The TensorFlow Object Detection API is used to detect many objects. We have Introduce an algorithm (YOLO). YOLO uses a similar phase while training, to match the appropriate anchor box with the bounding boxes of each ground truth object within an image. Essentially, the anchor box with the highest degree of flap with an object is responsible for predicting that object's class and its location. It has microcontroller which has wi-fi inbuilt module. This guide is convenient and offers data to the client to move around in new condition, regardless of whether indoor or open air, through an ease to use interface. Then again, and so as to lessen route challenges of the visually impaired, a deterrent location framework utilizing ultrasounds is added to this gadget. The proposed framework identifies the closest hindrance through ultrasonic sensors and it gives an alert to illuminate the visually impaired about its confinement.

Key Words: Python, Machine Learning, YOLO lib. Datasets, Opencv.