Computer Vision Basics

This course is designed to provide a solid foundation in computer vision techniques and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn the fundamental concepts and algorithms used in image processing, object detection, and image segmentation. The course covers various computer vision libraries and tools, enabling participants to develop practical skills in analyzing and interpreting visual data relevant to electricity generation, renewable energy management, and utility operations, such as satellite imagery, drone footage, and thermal images.

• Understand the fundamental concepts and techniques in computer vision

• Implement basic image processing operations, such as filtering, edge detection, and morphological transformations

• Apply object detection and image segmentation algorithms to identify and localize objects of interest in satellite imagery, drone footage, and thermal images

• Utilize computer vision libraries, such as OpenCV and scikit-image, for efficient image processing and analysis

• Develop computer vision applications for various Electricity Generation and Renewable Energy Plants & Utilities use cases, such as solar panel inspection, wind turbine monitoring, and power line fault detection

1. Introduction to Computer Vision

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

• Digital image fundamentals (pixels, color spaces, image formats)

• Image processing basics (reading, writing, and displaying images)

• Hands-on exercises: Basic image manipulation using Python and OpenCV

2. Image Processing Techniques

• Image filtering (smoothing, sharpening, and noise reduction)

• Edge detection (Sobel, Canny, and Laplacian operators)

• Morphological transformations (erosion, dilation, opening, and closing)

• Hands-on exercises: Implementing image processing techniques on satellite imagery, drone footage, and thermal images

3. Object Detection

• Template matching and its limitations

• Feature-based object detection (SIFT, SURF, and ORB)

• Cascade classifiers and Haar-like features for detecting solar panels and wind turbines

• Deep learning-based object detection (YOLO, SSD, and Faster R-CNN) for power line fault detection and equipment monitoring

• Hands-on exercises: Detecting objects of interest in satellite imagery, drone footage, and thermal images

4. Image Segmentation

• Thresholding techniques (global, adaptive, and Otsu's method)

• Region-based segmentation (region growing and split-and-merge)

• Edge-based segmentation (watershed and graph-based methods)

• Semantic segmentation using deep learning (FCN, U-Net, and DeepLab) for land cover classification and vegetation monitoring

• Hands-on exercises: Segmenting satellite imagery and drone footage for renewable energy site selection and monitoring

5. Applications and Advanced Topics

• Case studies of computer vision applications in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., solar panel inspection, wind turbine monitoring, power line fault detection)

• Introduction to 3D computer vision and point cloud processing for asset inspection and management

• Challenges and future directions in computer vision for electricity generation, renewable energy management, and utility operations

• Hands-on exercises: Developing a computer vision application for a specific Electricity Generation or Renewable Energy Plants & Utilities use case

• Strong understanding of linear algebra and calculus

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques