Advanced Computer Vision

This course is designed to provide an in-depth understanding of advanced computer vision techniques and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn cutting-edge methods for object detection, semantic segmentation, and 3D reconstruction, enabling them to develop and deploy sophisticated computer vision solutions for various tasks relevant to power systems, renewable energy monitoring, and grid asset management.

• Understand the principles and techniques behind advanced computer vision methods

• Implement and train state-of-the-art object detection models, such as Faster R-CNN, YOLO, and SSD, for power system component detection and monitoring

• Apply semantic segmentation algorithms, such as U-Net, DeepLab, and Mask R-CNN, for pixel-wise classification in aerial imagery and satellite data

• Reconstruct 3D models from 2D images using structure from motion (SfM) and multi-view stereo (MVS) techniques for grid asset inspection and management

• Develop and deploy advanced computer vision solutions for electricity generation, renewable energy monitoring, and grid optimization

1. Object Detection in Power Systems and Renewable Energy

• Overview of object detection and its applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Two-stage object detectors: R-CNN, Fast R-CNN, and Faster R-CNN

• Single-stage object detectors: YOLO, SSD, and RetinaNet

• Hands-on exercises: Implementing and training object detection models for power system component detection and monitoring

2. Semantic Segmentation for Aerial and Satellite Imagery

• Introduction to semantic segmentation and its importance in electricity generation and renewable energy

• Fully Convolutional Networks (FCNs) for semantic segmentation

• Encoder-decoder architectures: U-Net, SegNet, and DeepLab

• Instance segmentation with Mask R-CNN for solar panel and wind turbine detection

• Hands-on exercises: Applying semantic segmentation models to aerial and satellite imagery for renewable energy site selection and monitoring

3. 3D Reconstruction and Visualization for Grid Asset Management

• Principles of 3D reconstruction from 2D images

• Structure from Motion (SfM) and feature matching techniques

• Multi-View Stereo (MVS) and dense reconstruction methods

• Point cloud processing and mesh generation for grid asset inspection and management

• Hands-on exercises: Reconstructing 3D models of power lines, substations, and renewable energy installations

4. Advanced Topics and Applications

• Attention mechanisms and transformer-based models for power system and renewable energy anomaly detection

• Domain adaptation and transfer learning for cross-domain image analysis

• Unsupervised and self-supervised learning for representation learning in electricity generation and renewable energy data

• Case studies of advanced computer vision in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., predictive maintenance, grid resilience analysis)

• Hands-on exercises: Developing an advanced computer vision solution for a specific electricity generation or renewable energy use case

5. Deployment and Optimization

• Deploying computer vision models in production environments for power systems and renewable energy applications

• Optimizing models for real-time inference and edge deployment

• Monitoring and updating deployed models for continuous improvement

• Best practices for data management and version control in computer vision projects

• Hands-on exercises: Deploying an optimized computer vision model using a cloud platform (e.g., AWS, GCP)

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic computer vision concepts and techniques (e.g., image processing, feature extraction)

• Knowledge of convolutional neural networks (CNNs) and their applications