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所在平台: Udemy |
课程主页: https://www.udemy.com/course/automotive-camera-computer-vision-deep-learning-2a/
课程评论：没有评论
**课程名称：** 汽车摄像头 [计算机视觉，深度学习] - 2A **课程概述：** 本课程是“汽车摄像头”系列课程中的第二部分（2A），专注于汽车环境感知中至关重要的摄像头技术。随着深度学习和计算机视觉的飞速发展，基于摄像头的算法开发已发生根本性变化。该系列课程旨在系统地帮助学习者掌握从理论到实践的完整开发流程，尤其适合希望进入ADAS（高级驾驶辅助系统）和自动驾驶领域，并具备一定编程基础（Python 3.x）的背景人士。 **课程重点：** 本课程（2A）是该系列中侧重实践操作的一部分，将教授您： * **端到端摄像头感知流水线开发：** 利用面向对象编程（OOP）思想，在Python 3.x中设计和实现一个包含20多个类的完整摄像头感知流水线。 * **摄像头图像处理模块实现：** 逐步构建摄像头图像处理软件模块，能够加载ADAS车辆采集的真实摄像头数据，并进行预处理，为目标检测模块做好准备。 * **目标检测模块开发：** 深入学习并使用 Faster R-CNN、SSD、YOLOv5 和 YOLOv8 等先进模型，在Python中实现针对道路使用者（如行人、车辆等）的目标检测功能。 * **系统化学习路径：** 学习如何系统地开发基于摄像头的软件，为实际工作和项目做好准备。 **先修要求：** 这是一个高级课程，请确保您已满足所有列出的先修要求。 **学习建议：** * 仅想理解概念的学习者，可选择课程1。 * 希望深入理解概念并进行编程实践的学习者，建议完成课程1、课程2A和课程2B。 **免责声明：** 本课程中的算法仅供学习目的，在实际项目或工作中使用前，用户必须进行充分的测试、调试和根据具体需求进行修改。
Perception of the Environment is a crucial step in the development of ADAS (Advanced Driver Assistance Systems) and Autonomous Driving. The main sensors that are widely accepted and used include Radar, Camera, LiDAR, and Ultrasonic.This course focuses on Cameras. Specifically, with the advancement of deep learning and computer vision, the algorithm development approach in the field of cameras has drastically changed in the last few years.Many new students and people from other fields want to learn about this technology as it provides a great scope of development and job market. Many courses are also available to teach some topics of this development, but they are in parts and pieces, intended to teach only the individual concept.In such a situation, even if someone understands how a specific concept works, the person finds it difficult to properly put in the form of a software module and also to be able to develop complete software from start to end which is demanded in most of the companies.This series which contains 3 courses - is designed systematically, so that by the end of the series, you will be ready to develop any perception-based complete end-to-end software application without hesitation and with confidence.Course 1 (already published and available online) - focuses on theoretical foundations Course 2A (This course) - focuses on the step-by-step implementation of camera processing module and object detector modules using Python 3.x and object-oriented programming. course 2B (to be published very soon) - focuses on the step-by-step implementation of camera-based multi-object tracking (including Track object data structures, Kalman filters, tracker, data association, etc.) using Python 3.x and object-oriented programming. Course 2A - teaches you the following content (This course)In the complete course, you will develop a camera perception pipeline with 20+ classes using object-oriented programming in Python 3.x.You will implement a step-by-step camera image processing software module in Python 3.x to load real camera data collected from ADAS vehicles and preprocess it for the object detection module.You will develop a step-by-step complete object detection module in Python to detect various road users using FasterRCNN, SSD, YOLOv5, and YOLOv8.Learn systematically how to develop camera-based software for jobs and projects.[ATTENTION]:This is an advanced course in this field, so please fulfil all the stated prerequisites before you start this course. [Disclaimer]:Algorithms developed throughout this course are only for learning purposes. Learners must not use them directly in their projects or work without enough tests, debugging, and modifications according to requirements.[Suggestion]: Those who want to learn and understand concepts can take course 1 only. Those who want to learn and understand concepts and also want to know and do programming of those concepts should take all three courses 1, course 2A, and course 2B.
