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Automated Inspection with Computer Vision
Gain insights into automated inspection using computer vision. Learn image analysis, feature detection, 2D/3D transformations, and train neural networks for object detection and image segmentation.
4.8
54 Lessons
3 Projects
15h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Hands-on experience transforming image objects with OpenCV
- An understanding of feature detection and blob analysis
- The ability to use laser lines alongside 3D vision
- Hands-on experience in labeling image datasets using CVAT
- Working knowledge of convolutional neural networks for classification, object detection, and semantic segmentation with PyTorch
Learning Roadmap
2.
Getting Started with Images
Getting Started with Images
Get started with image I/O operations and annotation techniques in OpenCV.
3.
Color Spaces and Thresholding
Color Spaces and Thresholding
4 Lessons
4 Lessons
Work your way through color spaces, thresholding techniques, and adaptive thresholding for automated inspection.
4.
Smoothing and Masking
Smoothing and Masking
3 Lessons
3 Lessons
Enhance your skills in smoothing and masking techniques for improved automated image inspection.
5.
Detection of Features
Detection of Features
11 Lessons
11 Lessons
Solve problems in automated feature detection using template matching, morphology, blob, edge, and corner detection techniques.
6.
Image Registration
Image Registration
3 Lessons
3 Lessons
Tackle image registration using homography transformations and perspective warping for object inspection.
7.
3D Vision
3D Vision
4 Lessons
4 Lessons
Master the steps to 3D topography capture, calibration, and visualization using laser line systems.
8.
Getting Started with Neural Networks
Getting Started with Neural Networks
5 Lessons
5 Lessons
Step through neural networks, tensor manipulation, building blocks, training, and hands-on exercises.
9.
Convolutional Neural Networks
Convolutional Neural Networks
8 Lessons
8 Lessons
Walk through CNN fundamentals, training techniques, and leveraging pretrained networks for image classification.
10.
Object Detection and Semantic Segmentation
Object Detection and Semantic Segmentation
6 Lessons
6 Lessons
Work your way through object detection and semantic segmentation using pre-trained CNN models.
11.
Dataset Annotation
Dataset Annotation
4 Lessons
4 Lessons
Apply your skills to annotate datasets for semantic segmentation using CVAT tools and formats.
12.
Final Remarks
Final Remarks
2 Lessons
2 Lessons
Map out the steps for automated inspection using recipes and understand key distinctions in approaches.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Computer vision is essential for developers who wish to learn practical skills, e.g., in industrial manufacturing, automated inspection of products is crucial for quality assurance.
In this course, you’ll apply computer vision and machine learning to analyze images for automated inspection. You’ll start by learning image I/O operations, thresholding, smoothing, and masking. You’ll learn feature detection using template matching and morphology. You’ll use the Sobel and Canny Edge Detectors, Harris Corner Detector, and Hough transform. You’ll learn about 2D transformations, including perspective and affine transformation, and 3D-to-2D projections. You’ll perform topography with a laser line to inspect the 3D shape of an object. Lastly, you’ll train convolutional neural networks for object detection and image segmentation and annotate datasets using CVAT.
By the end of this course, you will have the skills to build inspection systems for industrial applications using computer vision and machine learning.
ABOUT THE AUTHOR
Sébastien Gilbert
Computer vision and machine learning engineer. I like to learn by building.
Trusted by 2.9 million developers working at companies
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Anthony Walker
@_webarchitect_
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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