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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-web-app-project-number-plate-detection-ocr/
课程评论：没有评论
课程名称：YOLO：自动车牌检测与文本提取应用 课程概述： 欢迎参加《车牌检测与光学字符识别：深度学习Web应用项目》，本课程将从零开始教授车牌检测和OCR（光学字符识别）技术。图像处理和物体检测是数据科学的重要领域，广泛应用于各个行业，需求逐渐增长。本课程涵盖了一系列建模技术，包括物体检测数据（图像）的标注、数据预处理、深度学习模型的构建（使用InceptionResNet V2）、模型评估和生产（Web应用）。 课程开始时，我们将讨论构建该应用的项目架构，然后介绍如何收集数据并使用开源图像标注工具为车牌进行图像标注。接下来，我们将处理数据，使用TensorFlow 2构建和训练深度学习物体检测模型（InceptionResNet V2）。经过最佳损失的模型训练后，我们将进行评估，计算模型的交并比（IoU）和精确度。 完成物体检测模型后，我们将从包含车牌的图像中裁剪出感兴趣区域（ROI），并将ROI传递给Python中的光学字符识别API Tesseract（Pytesseract），以提取图像中的文本。最后，我们将整合所有部分，构建完整的深度学习模型管道。在课程的最后模块中，我们将学习使用Flask创建Web应用项目。我们将首先学习Flask的基本概念，例如URL路由和模板渲染，然后使用HTML和Bootstrap创建网站，最终完成我们的应用。 您将学到的内容包括： - 使用Python编程构建项目 - 进行物体检测的图像标注 - 在TensorFlow 2.x中训练物体检测模型（InceptionResNet V2） - 模型评估 - 使用Pytesseract进行光学字符识别 - Flask API - 使用HTML、Bootstrap和Python进行Flask Web应用开发 - 使用自定义数据训练YOLO模型 - 开发Web应用并集成YOLO模型 计算机视觉基础的Web应用是一个常见的主题，您可以在问答环节提出问题，我们将乐于回答您的疑问。此外，我们提供了所有笔记本和程序文件作为参考资料。
Welcome to NUMBER PLATE DETECTION AND OCR: A DEEP LEARNING WEB APP PROJECT from scratchImage Processing and Object Detection is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers modeling techniques including labeling Object Detection data (images), data preprocessing, Deep Learning Model building (InceptionResNet V2), evaluation, and production (Web App)We start this course Project Architecture that was followed to Develop this App in Python. Then I will show how to gather data and label images for object detection for Licence Plate or Number Plate using Image Annotation Tool which is open-source software developed in python GUI (pyQT).Then after we label the image we will work on data preprocessing, build and train deep learning object detection model (InceptionResnet V2) in TensorFlow 2. Once the model is trained with the best loss, we will evaluate the model. I will show you how to calculate the Intersection Over Union (IoU) The precision of the object detection model.Once we have done with the Object Detection model, then using this model we will crop the image which contains the license plate which is also called the region of interest (ROI), and pass the ROI to Optical Character Recognition API Tesseract in Python (Pytesseract). In this model, I will show you how to extract text from images. Now, we will put it all together and build a Pipeline Deep Learning model. In the final module, we will learn to create a web app project using FLASK Python. Initially, we will learn basics concepts in Flask like URL routing, render the template, template inheritance, etc. Then we will create our website using HTML, Bootstrap. With that we are finally ready with our App.WHAT YOU WILL LEARN?Building Project in Python ProgrammingLabeling Image for Object DetectionTrain Object Detection model (InceptionResNet V2) in TensorFlow 2.xModel EvaluationOptical Character Recognition with PytesseractFlask APIFlask Web App Development in HTML, Boostrap, PythonTrain YOLO model with Custom dataDevelop web application and integrate YOLO ModelWe know that Computer Vision-Based Web App is one of those topics that always leaves some doubts. Feel free to ask questions in Q & A and we are very happy to answer all your questions.We also provided all Notebooks, py files in the resources which will useful for reference.
