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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-driver-distraction-drowsiness-detection-with-pythoncv/
课程评论：没有评论
课程名称：使用Python和计算机视觉进行AI驾驶员分心与困倦检测 课程概述： 欢迎参加本全面的实战课程，在这里您将学习如何开发一个智能的AI驱动监控系统，实时检测驾驶员的分心和困倦。课程将结合ResNet50模型进行分心检测，以及基于面部特征点的算法进行困倦检测，从而提供一个完整的道路安全和驾驶监控解决方案。 您将学习的内容包括： 1. **分心检测模块**： - 使用State Farm驾驶员分心数据集训练模型，以识别10种不同的分心行为，如发短信、进食、调整收音机或与乘客交谈。 - 利用TensorFlow/Keras训练ResNet50深度学习模型。 - 应用数据预处理、增强、迁移学习和超参数调优来提高模型准确性。 - 构建一个实时分心检测系统，使用OpenCV并与基于Tkinter的GUI和网络界面集成。 - 部署您的模型，以便在车队管理和AI安全系统等现实场景中使用。 2. **困倦检测模块**： - 使用Python和OpenCV捕捉和处理实时视频流。 - 利用MediaPipe提取面部特征点，分析眼睛和嘴巴的运动。 - 计算眼睛比率（EAR）和嘴巴比率（MAR），以检测疲劳、打哈欠和困倦的迹象。 - 实现逻辑，当检测到困倦时触发实时警报和视觉警示。 - 创建一个基于Tkinter的用户界面，实时显示状态和指标。 课程结束时，您将能够： - 构建一个双功能的驾驶员监控系统，能够检测分心和困倦。 - 获得AI、计算机视觉、深度学习和GUI开发的实战经验。 - 准备将您的项目部署到交通、物流和安全系统等现实应用中。 无论您是初学者还是中级Python开发者，本课程旨在为您提供构建AI驱动安全解决方案的有价值的实战经验。
AI-Powered Driver Monitoring System: Distraction and Drowsiness Detection using Python & Computer Vision Welcome to this all-in-one, hands-on course where you'll learn to develop an intelligent AI-powered system capable of detecting driver distractions and drowsiness in real-time using Python, Computer Vision, and Deep Learning.This course combines the power of ResNet50 for distraction detection and facial landmark-based algorithms for drowsiness detection, offering a complete solution for road safety and driver monitoring.What You'll Learn:Distraction Detection Module:Use the State Farm Driver Distraction dataset to train a model that identifies 10 different distraction activities such as texting, eating, adjusting the radio, or talking to passengers.Train a ResNet50 deep learning model using TensorFlow/Keras.Apply data preprocessing, augmentation, transfer learning, and hyperparameter tuning to improve model accuracy.Build a real-time distraction detection system using OpenCV and integrate it with a Tkinter-based GUI and web interface.Deploy your model for use in real-world scenarios like fleet management and AI safety systems.Drowsiness Detection Module:Capture and process real-time video feeds using Python and OpenCV.Extract facial landmarks using MediaPipe to analyze eye and mouth movements.Calculate Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to detect signs of fatigue, yawning, and drowsiness.Implement logic to trigger real-time alerts and visual warnings when drowsiness is detected.Create a Tkinter-based UI to display status and metrics in real-time.By the end of this course, you will:Build a dual-function Driver Monitoring System that detects both distractions and drowsiness.Gain practical, hands-on experience in AI, computer vision, deep learning, and GUI development.Be equipped to deploy your project in real-world applications across transportation, logistics, and safety systems.Whether you're a beginner or an intermediate Python developer, this course is designed to provide valuable, real-world experience in building AI-powered safety solutions.
