IJBTT

Object Recognition for Visually Impaired People
	© 2020 by IJCTT Journal
	Volume-68 Issue-8
	Year of Publication : 2020
	Authors : Kavitha Srinivasan, Shanmuga Velayutham V, Vignesh G, Subash R
	DOI :  10.14445/22312803/IJCTT-V68I8P105

How to Cite?

Kavitha Srinivasan, Shanmuga Velayutham V, Vignesh G, Subash R, "Object Recognition for Visually Impaired People," International Journal of Computer Trends and Technology, vol. 68, no. 8, pp. 33-38, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I8P105

Abstract
Deep learning techniques are evolving rapidly in computer vision for many real-time applications, namely object detection, recognition, classification, segmentation, prediction and analysis. In this paper, an object recognition model for visually impaired people is proposed and validated using deep learning techniques for multiple datasets. The proposed model identifies multiple objects in a frame with its corresponding text, and the identified objects are converted into speech to guide the visually impaired people in real-time. The object identification process is carried out using a bounding box technique and a single convolutional neural network. The resulting bounding boxes with less probability than the threshold are eliminated, and the remaining objects are identified using a pre-trained Darkflow model. Then the identified objects are mapped to relevant text and converted to speech using Text-to-Speech (TTS) tool. The proposed model has been validated using four types of datasets, such as Pascal VOC dataset, COCO dataset, BROID challenge dataset and Auto Rickshaw detection challenge dataset. The novelty of this work is: modified intersection over union algorithm for better recognition, chosen datasets have different sets of images, and the weight file is modified to recognize the objects of the challenge dataset. OpenCV and Compute Unified Device Architecture (CUDA) are used for image manipulation and graphics processing along with Tensorflow. The final output is obtained in audio format by applying TTS to the objects identified using Pyttsx, which is a python package that converts simple text to the speech signal.

Keywords
Object identification, Object recognition, YOLO, SSD, Intersection over Union, Darkflow, Text to speech.

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