CNN based Auto-Assistance System as a Boon for Directing Visually Impaired Person

Shah, Samkit; Bandariya, Jayraj; Jain, Garima; Ghevariya, Mayur; Dastoor, Sarosh

doi:10.1109/icoei.2019.8862699

Cited by 10 publications

(1 citation statement)

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“…Convolution neural networks for object detection systems for visually impaired or blind people were continually improved. Convolutional neural network and Haar cascade classifiers were compared by Shah et al [25] to conduct a suitable algorithm to assist the visually impaired or blind person for a real-time scenario. The dataset used in the training process of CNN was COCO 2017.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Rachburee

Punlumjeak

2021

IJECE

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The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.

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Section: Literature Review and Related Workmentioning

confidence: 99%