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Real-Time Pill Identification for the Visually Impaired Using Deep Learning

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arxiv 2405.05983 v1 pith:OXTYOUQH submitted 2024-05-08 cs.CV cs.AIcs.LG

Bo Dang , Wenchao Zhao , Yufeng Li , Danqing Ma , Qixuan Yu , Elly Yijun Zhu This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords applicationimpairedmobilepillvisuallyidentificationreal-timedeep
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The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to- Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. Keywords-Deep Learning; YOLO Framework; Mobile Application; Visual Impairment; Pill Identification; Healthcare

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient

    cs.RO 2024-11 reject novelty 3.0 of 10

    The paper claims that adding Frenet coordinates to DDPG reduces lateral tracking error in Gazebo simulations, but the evidence is qualitative, underspecified, and not reproducible.

  2. Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)

    cs.CV 2024-11 reject novelty 2.0 of 10

    A vague proposal for a CNN plus optical flow tracking method with unverifiable performance claims.

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