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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-drowsiness-detection-using-dlib/
课程评论：没有评论
**课程名称：** 人工智能：使用DLib检测疲劳驾驶 **课程概述：** 本课程旨在教授学员如何利用AI技术，特别是DLib库，开发一个能够检测驾驶员疲劳状况的系统。通过实际操作，您将学习如何使用预训练的DLib模型，一步一步地构建一个疲劳检测器。 **课程内容亮点：** * **DLib基础：** 了解DLib的基本概念、面部检测器（Face Detector）和面部区域预测器（Face Region Predictor）。 * **实战项目：** 这是一个动手实践课程，将指导您完成整个项目的构建过程。 * **核心技术：** 学习如何使用DLib提取面部关键点，计算眼部状态比（Eye Aspect Ratio），并通过欧几里得距离来判断驾驶员是否疲劳。 * **实时应用：** 将所学知识应用于实时视频流，实现疲劳状态的实时检测。 * **项目结构：** 课程详细介绍了项目文件夹结构，并引导您完成所有必要的库导入和模型加载。 * **警报功能：** 学习如何定义一个警报方法，以便在检测到疲劳时发出提示。 **课程结构（任务分解）：** 1. 项目概述 2. Google Colab入门 3. 理解项目文件夹结构 4. DLib简介 5. DLib面部检测器 6. DLib面部区域预测器 7. 库的导入 8. 加载DLib面部区域预测器 9. 定义面部区域坐标 10. 使用欧几里得距离计算眼部状态比 11. 加载面部检测器和面部关键点预测器 12. 使用面部区域坐标提取左右眼细节 13. 定义播放警报的方法 14. 整合所有功能，完成项目 **项目背景与意义：** 根据统计数据，驾驶员疲劳是交通事故的一个重要因素，尤其在高速公路上，约有10%-40%的事故与疲劳驾驶有关。本课程旨在通过构建疲劳检测器，为提升道路安全做出贡献。 **学习成果：** * 获得AutomationGig颁发的**结业证书**。 * 课程结束后，您将收到包含**Jupyter Notebook**和其他项目文件的项目资料。 **免责声明：** 本课程内容仅用于教育目的，请勿在真实世界场景中使用。 **立即报名，在几小时内掌握这项21世纪最热门的技能！**
If you want to learn the process to detect drowsiness while a person is driving a car with the help of AI then this course is for you.In this course I will cover, how to use a pre-trained DLib model to detect drowsiness. This is a hands on project where I will teach you the step by step process in building this drowsiness detector using DLib.This course will walk you through the initial understanding of DLib, About Dlib Face Detector, About Dlib Face Region Predictor, then using the same to detect drowsiness of a person in a live video stream.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Project Overview.Task 2 : Introduction to Google Colab.Task 3 : Understanding the project folder structure.Task 4 : What is DlibTask 5 About Dlib Face DetectorTask 6 About Dlib Face Region PredictorTask 7 : Importing the Libraries.Task 8 : Loading the dlib face regions predictorTask 9: Defining the Face region coordinatesTask 10: Using Euclidean distance to calculate the Eye Aspect RatioTask 11: Loading the face detector and face landmark predictorTask 12: Using the face region coordinates to extract the left and right eye detailsTask 13: Defining a method to play the alarm.Task 14: Putting it all together.Almost all the statistics have identified driver drowsiness as a high priority vehicle safety issue. Drowsiness has been estimated to be involved in 10-40 per cent of crashes on motorways. Fall-asleep crashes are very serious in terms of injury severity and more likely to occur in sleep-deprived individuals.Hence this problem statement has been picked up to see how we can solve this problem to a great extent by build a drowsiness detector.However please note, that this has been made purely for educational purpose and refrain from using the same in real world scenarios.In this course we are going to build a drowsiness detector and use the same to detect in live video streams.Take the course now, and have a much stronger grasp on the subject in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. The Jupyter notebook and other project files are provided at the end of the course in the last section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only][Music: bensound]
