Basics of Visionary Solutions for Automated Pipeline Inspection & Monitoring

This course is designed to provide a solid foundation in computer vision techniques and their applications in the Oil & Gas industry. 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 the Oil & Gas domain, such as seismic images, satellite imagery, and equipment inspection data.

• 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

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

• Develop computer vision applications for various Oil & Gas use cases, such as seismic interpretation and equipment inspection

1. Introduction to Computer Vision

   • Overview of computer vision and its applications in the Oil & Gas industry

   • 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 Oil & Gas images

3. Object Detection

   • Template matching and its limitations

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

   • Cascade classifiers and Haar-like features

   • Deep learning-based object detection (YOLO, SSD, and Faster R-CNN)

   • Hands-on exercises: Detecting objects of interest in oil and gas equipment 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)

   • Hands-on exercises: Segmenting seismic images and satellite imagery

5. Applications and Advanced Topics

   • Case studies of computer vision applications in the Oil & Gas industry (e.g., pipeline monitoring, drill bit wear analysis)

   • Introduction to 3D computer vision and point cloud processing

   • Challenges and future directions in computer vision for the Oil & Gas domain

   • Hands-on exercises: Developing a computer vision application for a specific Oil & Gas 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