Deep Learning Based Shopping Assistant For The Visually Impaired
Deep Learning Based Shopping Assistant For The Visually Impaired
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DOI：
10.1109/icce.2019.8662011
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Daniel Pintado;Vanessa Sanchez;Erin Adarve;Mark Mata;Zekeriya Gogebakan;Bunyamin Cabuk;Carter Chiu;J. Zhan;L. Gewali;Paul Y. Oh
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文献类型：
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作者：
Daniel Pintado;Vanessa Sanchez;Erin Adarve;Mark Mata;Zekeriya Gogebakan;Bunyamin Cabuk;Carter Chiu;J. Zhan;L. Gewali;Paul Y. Oh
计算机视觉和人工智能的当代发展有望大大改善残疾人的生活。在本文中，我们提出了一个这样的发展：可穿戴物体识别设备的眼镜的形式。我们的设备专门用于识别杂货店农产品区的物品，但可以作为任何类似物体识别可穿戴设备的概念验证。它是用户友好的，具有按钮，按下捕捉图像与内置相机。卷积神经网络（CNN）用于训练对象识别系统。在物体被识别之后，除了产品的价格之外，还利用文本到语音系统来通知用户他们持有的是哪个物体。准确率高达99.35%，我们的产品已被证明能够成功识别物体，其准确性高于现有模型。
Contemporary developments in computer vision and artificial intelligence show promise to greatly improve the lives of those with disabilities. In this paper, we propose one such development: a wearable object recognition device in the form of eyewear. Our device is specialized to recognize items from the produce section of a grocery store, but serves as a proof of concept for any similar object recognition wearable. It is user friendly, featuring buttons that are pressed to capture images with the built-in camera. A convolutional neural network (CNN) is used to train the object recognition system. After the object is recognized, a text-to-speech system is utilized to inform the user which object they are holding in addition to the price of the product. With accuracy rates of 99.35%, our product has proven to successfully identify objects with greater correctness than existing models.