Overview
Explore advanced techniques in deep learning for image segmentation and object detection, focusing on practical applications in autonomous driving.
Syllabus
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- Course Introduction
-- Overview of Semantic Segmentation
-- Importance in Satellite Imagery and Autonomous Driving
- Fundamentals of Image Segmentation
-- Introduction to Image Segmentation Techniques
-- Classical Methods vs. Deep Learning Approaches
- Deep Learning Basics
-- Neural Networks Overview
-- Convolutional Neural Networks (CNNs) Essentials
- Advanced Semantic Segmentation Techniques
-- Fully Convolutional Networks (FCNs)
-- U-Net Architecture
-- DeepLab Variants
- Data Preparation and Annotation
-- Satellite Imagery Datasets
-- Image Annotation Tools and Techniques
-- Data Augmentation for Segmentation Models
- Model Training and Optimization
-- Training Strategies and Best Practices
-- Loss Functions for Segmentation
-- Hyperparameter Tuning
- Evaluation Metrics
-- Intersection over Union (IoU)
-- Precision, Recall, and F1 Score
-- Model Evaluation in Real-world Scenarios
- Object Detection in Satellite Imagery
-- Introduction to Object Detection
-- YOLO and Faster R-CNN Architectures
-- Integrating Segmentation and Detection
- Applications in Autonomous Driving
-- Semantic Segmentation for Navigation
-- Detecting Vehicles, Pedestrians, and Obstacles
-- Challenges and Solutions in Real-time Processing
- Practical Implementation
-- Tools and Frameworks (e.g., TensorFlow, PyTorch)
-- Building a Segmentation Model from Scratch
-- Deploying Models on Edge Devices
- Case Studies and Industry Applications
-- Real-world Projects and Companies
-- Future Trends in Semantic Segmentation
- Capstone Project
-- Designing and Implementing Your Own Segmentation Solution
-- Presentation and Peer Review
- Course Summary and Next Steps
-- Recap of Key Concepts
-- Resources for Further Learning
Taught by
Tags
On-Demand
