使用机器学习的智能视觉软件应用

Saravanan Alagarsamy, Prudhivi Deepak, Lavanya M, T. G. Reddy, M. Kedareswari, A. Senthil Kumar
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引用次数: 0

摘要

智能视觉应用程序的前提是,有许多新兴的新技术在各自的领域表现出色。以下是目前正在使用的一些技术或模型:人体姿态估计、转向角度捕捉、车道检测和目标检测。这些都是用Open Pose和其他工具创建的各种方法和精湛的模型。由于这些系统中的每一个都具有独特的特征,因此在理解其工作原理之前分别构建每个系统是至关重要的。因为没有试用版可供消费者使用来学习模型如何工作,这些模型必须使用创建web应用程序的代码来构建,该应用程序将使学生能够通过使用设备上的相机来学习和体验每个模型的功能。对于可以使用自己的模型来运行、部署和测试的业务专业人员,而不仅仅是针对用户。列表中的每个模块都与自主导航有一定的联系。这些系统已被合并到一个Web应用程序中,以便学生可以轻松地对它们进行实验,并了解它们如何实时工作。因此,这个平台为学生和热情的学习者提供了极好的机会,可以与现场演示互动,并了解每个模型的功能。相信该Web应用程序将作为一个优秀的工具,供学生进行实验,并获得对上述计算机视觉模型操作的感觉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Vision Software Application using Machine Learning
The Smart Vision Application's premise is that there are numerous rising new technologies that are excelling in their fields. The following are a few of the technologies or models that are now in use: estimation of human pose, steering angle capture, lane detection, and object detection. These are all the various approaches and superb models created with Open Pose and other tools. Since each of these systems has unique characteristics, it is vital to separately construct each one before comprehending how it works. Because there is no trial version available for consumers to use to learn how the model works, these models must be constructed using codes creating a web application that will enable students to learn about and experience how each model functions by using the camera on their device. For business professionals who can use their own models to run, deploy, and test, not simply for users. Every module on the list has some connection to autonomous navigation. These systems have been combined into a single Web application so that students may easily experiment with them and see how they work in real-time. As a result, this platform presents excellent opportunities for students and enthusiastic learners to interact with the live demo and understand how each model functions. It is believed that the Web application will serve as an excellent tool for students to experiment with and gain a feel for the operation of the aforementioned computer vision models.
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