Overview
Automated visual inspection is crucial for efficient quality control in production systems and is widely used in manufacturing and infrastructure industries. Recent advancements in deep learning have introduced new tools for automated visual inspection tasks with high accuracy and robustness. These new methods enable the detection of defects without relying on failure data during training, offering unprecedented capabilities for detecting flaws on different manufactured surfaces.
In this session, we will introduce state-of-the-art approaches for visual inspection especially anomaly detection and object detection methods that are included in Computer Vision Toolbox Automated Visual Inspection Library. Through a couple of user case studies, you will learn AI DevOps workflow, some tips and techniques that can be used in your project. You will see how to integrate a visual inspection algorithm into a larger system.
Highlights
- Preprocessing and augmenting image data
- Anomaly detection and object detection
- Overview of DevOps AI Workflow with Apps for low-code AI
- Automatically generate code and deploy to embedded targets
- Validating and operationalizing models
