Summary of Real-time Pill Identification For the Visually Impaired Using Deep Learning, by Bo Dang et al.
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
by Bo Dang, Wenchao Zhao, Yufeng Li, Danqing Ma, Qixuan Yu, Elly Yijun Zhu
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a mobile application that uses deep learning to help individuals with visual impairments identify pills in real-time. The app, developed using the YOLO framework, processes images on mobile devices to recognize and differentiate between various pill types. The system also incorporates Text-to-Speech (TTS) for auditory feedback, enhancing usability and independence for visually impaired users. The study evaluates the application’s detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This mobile app helps people with visual impairments identify pills correctly. It uses special computer algorithms to look at pictures of pills on a phone or tablet, then tells the person what kind of pill it is through speech. This makes it easier for them to take their medicine safely and independently. The study showed that this app can accurately identify pills and is user-friendly. |
Keywords
» Artificial intelligence » Deep learning » Yolo
