Advanced Computer Vision

This course is designed to provide an in-depth understanding of advanced computer vision techniques and their applications in Production Control and Operations (P&OC). 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 production monitoring, quality control, and inventory 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 production monitoring and quality control

• Apply semantic segmentation algorithms, such as U-Net, DeepLab, and Mask R-CNN, for pixel-wise classification in production environments

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

• Develop and deploy advanced computer vision solutions for P&OC applications, such as defect detection, assembly line optimization, and warehouse management

1. Object Detection in Production Environments

• Overview of object detection and its applications in Production Control and Operations

• 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 production monitoring and quality control

2. Semantic Segmentation for Production Analysis

• Introduction to semantic segmentation and its importance in P&OC

• Fully Convolutional Networks (FCNs) for semantic segmentation

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

• Instance segmentation with Mask R-CNN for object-level analysis in production environments

• Hands-on exercises: Applying semantic segmentation models to production images for defect detection and assembly line optimization

3. 3D Reconstruction and Visualization for Inventory 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 inventory management and product inspection

• Hands-on exercises: Reconstructing 3D models of products and warehouse environments

4. Advanced Topics and Applications in P&OC

• Attention mechanisms and transformer-based models for production anomaly detection

• Domain adaptation and transfer learning for cross-domain image analysis in P&OC

• Unsupervised and self-supervised learning for representation learning in production data

• Case studies of advanced computer vision in Production Control and Operations (e.g., predictive maintenance, assembly line optimization)

• Hands-on exercises: Developing an advanced computer vision solution for a specific P&OC use case

5. Deployment and Optimization for P&OC

• Deploying computer vision models in production environments for P&OC 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

• Knowledge of production control and operations management principles