Gain insights into automated inspection with computer vision. Learn image analysis, feature detection, edge detection, transformations, and neural networks to build industrial inspection systems for quality assurance.

Automated Inspection with Computer Vision.png

AICV_jupyterOpenCV.tar.gz

load_image

annotation

color_spaces

thresholding

smoothing

masking

jup_smooth

morphology

blob_detection

jup_fiducials

edge_detection

jupyter_masking_edge_detection

corner_detection

jupyter_corner_detection

homographies

jup_homographies

topography

jup_topography

tensor_manipulation

neural_network_building_blocks

training_neural_network

jup_train_nn

cnn_building_blocks

cnn_for_classification

pretrained_classification_cnn

jup_train_classification_cnn

transfer_learning

jup_finetune_classification_CNN

cnn_for_object_detection

jup_pretrained_object_detection

cnn_for_semantic_segmentation

jup_pretrained_semantic_segmentation

annotation_formats

spa_train_mlp

prj_jup_create_train_mlp_classification

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.

Automated Inspection with Computer Vision

**(True or False)** Image classification provides information about the object's location in the image.

(True or False) Image classification provides information about the object’s location in the image.

We need to obtain a mask of the surface of cookies, to make sure that each cookie has a filled disk shape.

We need to locate the stickers in an image of multiple bottles.

We need to identify the location of pedestrians in an image of a street scene, taken by the camera of an autonomous vehicle.

We need to decide if an image of a single object is of product A or product B.

**(Select all that apply.)** Which scenario would be a use case for an object detection CNN?

(Select all that apply.) Which scenario would be a use case for an object detection CNN?

**(True or False)** Faster-RCNN and SSDlite CNNs accept a batch of images in the form of a list of single image tensors.

(True or False) Faster-RCNN and SSDlite CNNs accept a batch of images in the form of a list of single image tensors.

The number of categories that this CNN can detect

The number of image tensors passed as a batch

The number of channels of the input image

After calling the ```forward()``` method of an object detection CNN, we get a list of dictionaries. What corresponds to the length of the list?

After calling the <code>forward()</code> method of an object detection CNN, we get a list of dictionaries. What corresponds to the length of the list?

`(4, F)`, where `F` is the number of found objects.

`(F, 4)`, where `F` is the number of found objects.

`(C, F)`, where `C` is the number of categories, and `F` is the number of found objects.

`(F, C)`, where `F` is the number of found objects, and `C` is the number of categories.

The return of the ```forward()``` call of an object detection CNN is a list of dictionaries. One of the objects in each dictionary is a tensor mapped with the ```boxes``` key. What is the shape of this tensor?

The return of the <code>forward()</code> call of an object detection CNN is a list of dictionaries. One of the objects in each dictionary is a tensor mapped with the <code>boxes</code> key. What is the shape of this tensor?

```(x, y, w, h)```, where ```(x, y)``` is the top left corner, and ```(w, h)``` are the width and height.

```(cx, cy, w, h)```, where ```(cx, cy)``` is the rectangle center, and ```(w, h)``` are the width and height.

```(x1, y1, x2, y2)```, where ```(x1, y1)``` and ```(x2, y2)``` are opposite corners of a rectangle.

```(cx, cy, d, theta)```, where ```(cx, cy)``` is the center, ```d``` is the diagonal, and ```theta``` is the diagonal angle.

How are bounding boxes defined, in the tensor mapped with the ```boxes``` key?

How are bounding boxes defined, in the tensor mapped with the <code>boxes</code> key?

It is a number between 0 and 1, indicating the level of confidence about the identification of a given object.

It is a measurement of how well represented the corresponding category is in the training dataset.

It is a measurement of the object surface in the image.

It is a random number assigned to each object, for a unique identification.

What is the meaning of the ```scores``` values in the results dictionary?

What is the meaning of the <code>scores</code> values in the results dictionary?

Introduction

Getting Started with Images

Image I/O and Annotations

Color Spaces and Thresholding

Convert Color Spaces, Threshold

Smoothing and Masking

Detection of Features

Image Registration

3D Vision

Getting Started with Neural Networks

Convolutional Neural Networks

Project: Create and Train a CNN for Classification

Object Detection and Semantic Segmentation

Dataset Annotation

Final Remarks

Quiz: Pretrained Object Detection CNNs