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所在平台: Udemy |
课程主页: https://www.udemy.com/course/detecting-car-speed-empty-parking-spot-with-pytorch-cnn/
课程评论：没有评论
课程名称：使用Pytorch和CNN检测汽车速度和空停车位 课程概述：欢迎来到“使用Pytorch和CNN检测汽车速度和空停车位”课程。这是一门以项目为基础的综合课程，您将逐步学习如何构建先进的汽车速度检测系统和空停车位查找器，使用OpenCV、卷积神经网络（CNN）和Pytorch。该课程完美结合了计算机视觉与动作检测，为您提供了一个练习编程技能的绝佳机会，同时将先进的计算机视觉技术整合到交通管理中，并为城市交通领域的未来创新打开大门。 在介绍环节中，您将了解计算机视觉在交通管理中的应用，包括使用案例、使用的技术及一些技术限制。在接下来的课程中，您将学习汽车速度检测系统的工作原理，包括车辆检测、轨迹估计、速度计算和限速检查。此外，您还将学习空停车位检测系统的工作流程，从数据收集到停车占用分类。 在开始项目之前，我们将从Kaggle下载一个训练数据集，该数据集包含了成百上千张占用和未占用停车位的图像。我们将使用此数据集来训练模型，以区分已被占用和未被占用的停车位。 项目分两个部分进行：首先，您将逐步构建一个使用OpenCV和Pytorch的车辆速度检测系统，并设置速度限制。当有车辆超过速度限制时，系统会立即发送通知并开具超速罚单。其次，您将构建一个使用OpenCV和卷积神经网络的空停车位检测系统。 最后，课程还将进行测试，确保这两个检测系统正常工作，且所有编程逻辑得以正确实施。课程将帮助您更深入地理解为什么构建汽车检测系统和空停车位检测系统是重要的，特别是在提高交通安全和减少城市停车搜索时间方面的意义。 学习内容包括： - 计算机视觉在交通管理中的应用 - 汽车速度检测系统的工作原理 - 空停车位检测系统的工作流程 - OpenCV视频播放、运动检测和图像处理 - 创建检测速度的功能 - 使用OpenCV、Pytorch和单次检测器构建车辆速度检测系统 - 设置速度限制并检查超速 - 发放超速罚单的程序 - 使用OpenCV构建空停车位检测系统 - 使用Keras和卷积神经网络训练空停车位检测系统 - 进行准确度和性能测试。 这个课程将为您提供关于交通管理和智能城市设计的重要知识和技能，推动未来的职业发展。
Welcome to Detecting Car Speed & Empty Parking Spot with Pytorch & CNN course. This is a comprehensive project based course where you will learn step by step on how to build a cutting edge car speed detection system and empty parking spot finder using OpenCV, Convolutional Neural Network, and Pytorch. This course is a perfect combination between computer vision and motion detection, making it an ideal opportunity for you to practice your programming skills while integrating advanced computer vision technologies into traffic management and also open doors for future innovations in urban transportation. In the introduction session, you will learn about computer vision applications in traffic management, such as getting to know its use cases, technologies that will be used, and some technical limitations. Then, in the next session, you learn how the car speed detection system works? This section will cover vehicle detection, trajectory estimation, speed calculation, and speed limit check. In addition, you will also learn how empty parking lot detection systems work. This section will cover the full process from data collection to parking occupancy classification. Before starting the project, we will download a training dataset from Kaggle, the dataset contains hundreds or even thousands of images of occupied parking lots and unoccupied parking lots. We will use this dataset to train the model to be able to distinguish which parking lot has been occupied and which ones have not been occupied by cars. Once everything is ready, we will start the project section, in the first section, you will be guided step by step on how to build a vehicle speed detection system using OpenCV and Pytorch. In addition to that, we will also set a speed limit, so, whenever there is a car exceeding the speed limit, the system will immediately send you a notification and issue a speeding ticket. Meanwhile, in the second project, you will build an empty parking lot detection system using OpenCV and Convolutional Neural Network. Once we have built those detection systems, we will be conducting testing to make sure that they have been fully functioning and all programming logics have been implemented correctly.First of all, before getting into the course, we need to ask ourselves this question: why should we build a car detection system and empty parking lot detection system? Well, here is my answer, regarding the speed detection system, its implementation can significantly aid law enforcement agencies in enforcing speed limits and enhancing road safety. By accurately detecting and recording vehicle speeds, law enforcement officers can effectively identify and address instances of speeding, thereby reducing the risk of accidents and promoting safer driving behaviors. Moreover, the data collected by the speed detection system can serve as valuable evidence in prosecuting traffic violations, ensuring accountability and deterrence among drivers.On the other hand, the empty parking lot detection system offers numerous benefits to individuals and communities. By providing real-time information on available parking spaces, this system helps to reduce time wasted searching for parking, particularly in densely populated urban areas.Below are things that you can expect to learn from this course:Learn about computer vision applications in traffic management, such as getting to know its use cases, technical limitations, and technologies that will be usedLearn how a car speed detection system works. This section will cover vehicle detection, trajectory estimation, speed calculation, speed limit check, and speed ticket generatorLearn how empty parking spot detection systems work. This section will cover data collection, image preprocessing, feature extraction, object detection, and occupancy classificationLearn how to play video using OpenCVLearn how to detect motion using OpenCVLearn how to perform image processing using OpenCVLearn how to create function to detect speedLearn how to build car speed detection system using OpenCV, Pytorch, and Single Shot Multibox DetectorLearn how to set speed limit and check if the speed exceeds the speed limitLearn how to create and issue speeding ticketLearn how to calculate frame rate using OpenCVLearn how build empty parking spot detection system using OpenCVLearn how to train empty parking spot detection system using Keras and Convolutional Neural NetworkLearn how to create function to count how many empty parking spotLearn how to extract parking spot coordinate using OpenCVLearn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems
