Smart Glasses for Visually Impaired Using Image Processing Techniques

Karthikayan, P. N.; Pushpakumar, R.

doi:10.1109/i-smac52330.2021.9640715

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…T. Sharma [19] suggested a cane to detect obstacles and mud only. Using different image processing techniques, P. N. Karthikeyan [20] improved smart glasses for VIB individuals based on image analysis.…”

Section: Discussionmentioning

confidence: 99%

An Integrated IoT Smart Cane for the Blind and Visually Impaired Individuals

Abuelmakarem,

Abuelhaag,

Raafat

et al. 2024

SVU-International Journal of Engineering Sciences and Applicati

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The advancement of Internet of Things (IoT) technologies has paved the way for innovative solutions in various domains, including assisting visually impaired and blind (VIB) individuals. Smart canes with IoT capabilities enhance safety by providing real-time information about the user's surroundings, enabling them to avoid obstacles and potential hazards. This scientific article develops an intelligent cane for visually impaired and blind (VIB) people integrated with an IoT system. This smart device promotes independence and confidence for VIB individuals. The smart cane was designed using SolidWorks 3D modelling software. It detects water, obstacles, and fire. The IoT smart cane employs ultrasonic sensors to help VIB users go up and down the stairs and detect obstacles. The system is controlled via the Arduino module. The cane automatically notifies VIB users when it detects hazards. The family tracks the individual through the fixed MCU Node that sends the navigation to the family smartphone on the Blynk application. The system is calibrated versus different objects, and the blind location is sent to the family successfully through the Blynk mobile Application.

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Section: Discussionmentioning

confidence: 99%

An Integrated IoT Smart Cane for the Blind and Visually Impaired Individuals

Abuelmakarem,

Abuelhaag,

Raafat

et al. 2024

SVU-International Journal of Engineering Sciences and Applicati

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“…With only traffic lights in the picture, the highest accuracy of the traffic light recognition system is only 90.90%, and the recognition time is approximately 7-20 s, which indicates low accuracy and poor real-time performance. P. N. Karthikayan et al [17] developed a software for smart glasses to detect traffic lights using You Only Look Once (YOLO), an object detection algorithm based on deep learning. Then, the colors (red, green) in the image are detected using OpenCV.…”

Section: Vision-based Systemsmentioning

confidence: 99%

Design and Implementation of an Intelligent Assistive Cane for Visually Impaired People Based on an Edge-Cloud Collaboration Scheme

Yan-qing

Zhang

et al. 2022

Electronics

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Visually impaired people face many inconveniences in daily life, and there are problems such as high prices and single functions in the market of assistance tools for visually impaired people. In this work, we designed and implemented a low-cost intelligent assistance cane, particularly for visually impaired individuals, based on computer vision, sensors, and an edge-cloud collaboration scheme. Obstacle detection, fall detection, and traffic light detection functions have been designed and integrated for the convenience of moving for visually impaired people. We have also designed an image captioning function and object detection function with high-speed processing capability based on an edge-cloud collaboration scheme to improve the user experience. Experiments show that the performance metrics have an aerial obstacle detection accuracy of 92.5%, fall detection accuracy of 90%, and average image retrieval period of 1.124 s. It proves the characteristics of low power consumption, strong real-time performance, adaptability to multiple scenarios, and convenience, which can ensure the safety of visually impaired people when moving and can help them better perceive and understand the surrounding environment.

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