瞌睡检测和警报系统

Manish Mate, Abhishek Sahu, Atharva Kadam, Rajat Tandulkar, Arpita Agarwal
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引用次数: 0

摘要

嗜睡检测是一种识别个人疲劳或嗜睡迹象的解决方案。我们的模型的主要特点之一是,它可以使用移动摄像头(红外传感器)检测夜间的瞌睡情况。该系统捕捉人脸的红外图像,并分析与瞌睡有关的生理和行为线索。红外线传感器可在弱光条件下检测瞌睡情况,因此特别适用于夜间驾驶等夜间场景。一旦检测到瞌睡,系统就会触发警报或干预措施,帮助防止事故或错误的发生。我们将在模型中使用 OpenCV、TensorFlow、CNN 和 VGG19 功能等库。通过将安卓设备的易用性与深度学习算法的先进功能相结合,使用红外传感器进行嗜睡检测有可能大大提高个人日常生活的安全性和工作效率。
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Drowsiness Detection and Alert System
Drowsiness detection is a solution for identifying signs of fatigue or sleepiness in individuals. One of the key features of our model is that it can detect drowsiness at night as well using Mobile cameras (infrared sensors). The system captures infrared images of the person's face and analyzes the physiological and behavioral cues related to drowsiness. Infrared sensors allow for drowsiness detection in low-light conditions, making it particularly useful for night-time scenarios such as night driving. The system can trigger alerts or interventions if drowsiness is detected, helping to prevent accidents or mistakes. We will be using libraries like OpenCV, TensorFlow, CNN, and VGG19 features in our model. By combining the accessibility of Android devices with the advanced capabilities of the Deep Learning algorithm, drowsiness detection using infrared sensors has the potential to greatly improve the safety and productivity of individuals in their daily lives.
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