Special Cane with Visual Odometry for Real-time Indoor Navigation of Blind People

Tang, Jingyu; Hu, Menghan; Li, Guodong; Li, Qingli; Zhang, Jian; Zhou, Xiaofeng; Zhai, Guangtao

doi:10.1109/vcip49819.2020.9301782

Cited by 5 publications

(2 citation statements)

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“…Zhang et al designed an intelligent cane 8 that uses GPS positioning to provide voice guidance and can quickly contact family members in case of danger. Jing Yutang et al designed an auxiliary cane 9 with visual odometry that can help blind people navigate safely indoors. Guo et al developed an intelligent cane system 10 that calculates the distance between obstacles and the user using ultrasonic waves, and uses deep learning to train a neural network to recognize pedestrian crossings and blind alleys.…”

Section: Introductionmentioning

confidence: 99%

An improved method for blind navigation based on image-to-sound conversion

Miao,

Wang,

Liu

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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For a long time, blind or visually impaired people have relied on traditional aids such as canes or guide dogs to assist them in walking, but these aids have significant limitations. In order to help visually impaired individuals walk independently, this article improves the method of mapping images to sound in the literature, making it more applicable to blind navigation. By limiting the range and size of captured images, as well as improving the mapping relationship between captured images and transformed sound, the accuracy of the method has been improved from 84.2% to 88.8%, an increase of nearly 5% compared to the method in the literature, which can better assist in guiding the blind.

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Section: Introductionmentioning

confidence: 99%

An improved method for blind navigation based on image-to-sound conversion

Miao,

Wang,

Liu

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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“…Machine learning technology has been a popular field in recent years. It has made significant progress and has been widely used in many areas, such as computer vision [6][7][8][9] and real-time systems [10][11][12][13]. At present, many researchers have used machine learning methods for the diagnosis of CT images, some of which perform well and surpass human radiologists in many indicators.…”

Section: Introductionmentioning

confidence: 99%

WE-BA: Covid-19 detection by Wavelet Entropy and Bat Algorithm

Yu,

Pei,

Wang

et al. 2023

EAI Endorsed Trans Perv Health Tech

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Covid-19 is a kind of fast-spreading pneumonia and has dramatically impacted human life and the economy. As early diagnosis is the most effective method to treat patients and block virus transmission, an accurate, automatic, and effective diagnosis method is needed. Our research proposes a machine learning model (WE-BA) using wavelet entropy for feature extraction to reduce the excessive features, one-layer FNNs for classification, 10-fold cross-validation (CV) to reuse the data for the relatively small dataset, and bat algorithm (BA) as a training algorithm. The experiment eventually achieved excellent performance with an average sensitivity of 75.27% ± 3.25%, an average specificity of 75.88% ± 1.89%, an average precision of 75.75% ± 1.06%, an average accuracy of 75.57% ± 1.21%, an average F1 score of 75.47% ± 1.64%, an average Matthews correlation coefficient of 51.20% ± 2.42%, and an average Fowlkes–Mallows index of 75.49% ± 1.64%. The experiments showed that the proposed WE-BA method yielded superior performance to the state-of-the-art methods. The results also proved the potential of the proposed method for the CT image classification task of Covid-19 on a small dataset.

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