Summary of Detection Of a Facemask in Real-time Using Deep Learning Methods: Prevention Of Covid 19, by Gautam Siddharth Kashyap et al.
Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19
by Gautam Siddharth Kashyap, Jatin Sohlot, Ayesha Siddiqui, Ramsha Siddiqui, Karan Malik, Samar Wazir, Alexander E. I. Brownlee
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel-coronavirus disease (Covid-19) pandemic has spread rapidly worldwide, prompting the World Health Organisation (WHO) to recommend face masks as an effective preventive measure. To monitor and enforce mask-wearing compliance in crowded areas, this paper proposes a deep learning-based technique for real-time detection of facemasks on individuals. The model can handle single or multiple people in frames captured via webcam, including scenarios with still or moving subjects, and even in low-light conditions (nightlight). Notably, the proposed approach achieves an accuracy range of 74% to 99% compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary During a global health crisis like Covid-19, it’s crucial to wear face masks. But how can we ensure everyone is wearing one? This paper develops a new way to automatically detect people wearing masks using computer vision techniques. The method works in different situations, such as when there are multiple people or when the lighting is low. It’s an important tool to help stop the spread of Covid-19. |
