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.

Beginner

54 Lessons

15h

Certificate of Completion

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.

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Explanations

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This course includes

1 Project

2 Assessments

124 Playgrounds

22 Quizzes

This course includes

1 Project

2 Assessments

124 Playgrounds

22 Quizzes

Course Overview

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 De...Show More

TAKEAWAY SKILLS

Python Programming

Pytorch Basics

Machine Learning

Neural Networks

Pytorch

Opencv

Automatedinspection

Computervision

What You'll Learn

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

What You'll Learn

Hands-on experience transforming image objects with OpenCV

Course Content

Introduction

Get familiar with automated inspection using computer vision and its practical applications.

Automated Inspection: A Major Application of Computer Vision

Getting Started with Images

Get started with image I/O operations and annotation techniques in OpenCV.

I/O Operations on Images Image Annotation Quiz: Image I/O and Annotations

Image I/O and Annotations

Assessment

Color Spaces and Thresholding

Work your way through color spaces, thresholding techniques, and adaptive thresholding for automated inspection.

Color Spaces Thresholding Adaptive Threshold Quiz: Color Spaces, Thresholding, and Adaptive Threshold

Convert Color Spaces, Threshold

Assessment

Smoothing and Masking

Enhance your skills in smoothing and masking techniques for improved automated image inspection.

Smoothing Masking Exercise: Smooth and Mask in Jupyter

Detection of Features

Solve problems in automated feature detection using template matching, morphology, blob, edge, and corner detection techniques.

Template Matching Morphology Blob Detection Quiz: Template Matching, Morphology, and Blob Detection Exercise: Detect Fiducials on an Electronic Board Edge Detection Quiz: Edge Detection, Thinning, and Hough Lines Exercise: Detect Edges of an Electronic Board Corner Detection Quiz: Corner Detection Exercise: Detect Corners

Image Registration

3 Lessons

Tackle image registration using homography transformations and perspective warping for object inspection.

3D Vision

4 Lessons

Master the steps to 3D topography capture, calibration, and visualization using laser line systems.

Getting Started with Neural Networks

5 Lessons

Step through neural networks, tensor manipulation, building blocks, training, and hands-on exercises.

Convolutional Neural Networks

8 Lessons

Walk through CNN fundamentals, training techniques, and leveraging pretrained networks for image classification.

Project: Create and Train a CNN for Classification

Project

10.

Object Detection and Semantic Segmentation

6 Lessons

Work your way through object detection and semantic segmentation using pre-trained CNN models.

Cats vs Dogs Classification with Convolutional Neural Networks

Project

11.

Dataset Annotation

4 Lessons

Apply your skills to annotate datasets for semantic segmentation using CVAT tools and formats.

12.

Final Remarks

2 Lessons

Map out the steps for automated inspection using recipes and understand key distinctions in approaches.

Recognize Handwritten Digits Using a Deep Neural Network

Project

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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