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Computer Vision




Python, OpenCV for computer vision tasks, TensorFlow or PyTorch for CNN-based approaches.

Skills to be Learned:

Core computer vision principles for lane detection.

Lane Detection

Dive into the realm of computer vision and deep learning with this project-based course, where you'll learn the intricacies of lane detection crucial for autonomous vehicles and robotics.

This is a project-based course focusing on computer vision techniques and algorithms used to detect and track lanes on roadways. Lane detection plays a crucial role in autonomous vehicles, driver-assistance systems, and robotics. This project is designed to equip participants with the knowledge and skills required to develop robust lane detection systems that can identify and track lanes in images and videos captured by cameras mounted on vehicles. Students will delve into computer vision concepts, image processing, and deep learning to create accurate and reliable lane detection models. By the end of this course, participants will be capable of building their own lane detection systems for various applications in the automotive and robotics industries.

Learning Outcomes:

Upon successful completion of this course, students will:

- Gain a deep understanding of computer vision and its applications in lane detection.

- Master image preprocessing and enhancement techniques for better lane detection.

- Develop proficiency in programming with Python and working with OpenCV.

- Learn traditional computer vision approaches for lane detection.

- Explore deep learning techniques for lane detection using convolutional neural networks (CNNs).

- Implement and fine-tune lane detection models.

- Evaluate and compare the performance of different lane detection algorithms.


- Proficiency in Python programming.

- Basic knowledge of image processing and computer vision concepts.

- Familiarity with deep learning concepts is beneficial but not mandatory.

- Prior experience with OpenCV or related libraries is helpful but not required.

Libraries and Programming Language Used:

- Programming Language: Python

- Computer Vision Library: OpenCV

- Deep Learning Framework: TensorFlow or PyTorch (for CNN-based approaches)

- Numerical Computing: NumPy

- Data Visualization: Matplotlib

Course Syllabus:

Introduction to Lane Detection

   - Significance and applications of lane detection in autonomous vehicles and robotics.

   - Overview of computer vision techniques for lane detection.

Setting Up the Development Environment

   - Installing Python and required libraries.

   - Configuring the environment for computer vision and deep learning projects.

Image Preprocessing for Lane Detection

   - Techniques for enhancing lane features in images.

   - Dealing with challenging lighting and environmental conditions.

Traditional Lane Detection Methods

   - Understanding classical computer vision algorithms for lane detection, such as Hough Transform.

   - Implementation and evaluation of traditional methods.

Lane Detection using Deep Learning

   - Introduction to convolutional neural networks (CNNs) for lane detection.

   - Building and training CNN models for lane detection tasks.

Evaluating Lane Detection Models

   - Evaluation metrics for lane detection accuracy.

   - Quantitative and qualitative assessment of model performance.

   - Visualizing the results

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