Computer Vision - CNN for image classification

One of the most important uses of self-driving automobiles is image categorization.

Where the semi and completely autonomous car industries are rapidly expanding, we now see self-driving cars from the likes of Tesla and Kia moving around everywhere.

We will be exploring the technology behind this and how computers see and classify images, from understanding waste types to identifying humans on the road against inanimate objects and signs.

We will build a traffic sign classification model using CNN.

Transcript

1. Computer Vision - CNN for Image Classification by Wale Ayandiran Presented at AI Wales meetup, Tramshed Tech, Cardiff. 08-06-2022
2. We might start by identifying certain pictures of animals ourselves. Shall we? How does computer identify images with their labels?
3. Can you tell these wild cats aparts?
4. Leopard Cheetah Jaguar Tiger Image credit: Google
5. Machine Learning
6. Lion Cat Wolf Dog Fox
7. Cat Lion Dog Arctic Fox Wolf Image credit: Google
8. How are we as humans able to differentiate things easily?
9. Recognizing Patterns How are we as humans able to differentiate things easily?
10. Credit: MIT Feature Detection Low level feature detection to high level feature detection
11. What do computers see?
12. Images are matrix of numbers
13. Images are matrix of numbers What computers see
14. Images are matrix of numbers What computers see Credit: https://openframeworks.cc/ofBook/chapters/image_processing_computer_vision.html
15. Credit: https://www.keithbcarter.com/think-differently/how-a-machine-sees Computer detects features of an image as matrix of numbers.
16. How do we make computers identify images? Neural Networks. Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve. Credit: https://www.sas.com/en_us/insights/analytics/neural-networks.html
17. A simple neural network includes an input layer, an output (or target) layer and, in between, a hidden layer. The layers are connected via nodes, and these connections form a “network” – the neural network – of interconnected nodes. How Neural Network Works Credit: https://www.sas.com/en_us/insights/analytics/neural-networks.html
18. • Artificial Neural Network (ANN) • Convolutional Neural Network (CNN) • Recurrent Neural Network (RNN) 3 Popular Neural Networks
19. Convolutional Neural Networks
20. Learning Visual Features ➔ Filter extraction Applying filters and generating smaller pieces of image from the original image. ➔ Features Mapping Using the filters extracted to create a feature map of the image.
21. Image credit: MIT
22. Tiger Tiger’s eyes ? = Y Tiger’s nose ? = Y Tiger’s mouth/whiskers ? = Y Tiger’s paws ? = Y Tiger’s tail ? = Y Tiger’s head ? = Y Tiger’s body ? = Y Is it a Tiger ? = Y Image credit: Google
23. Main parts of a CNN Three main parts to a CNN ➔ Convolution Application of filters to generate feature maps ➔ Non-linearity (ReLU) Replacing negative values with zero (0) often ReLU (Rectified Linear Unit) ➔ Pooling Reduces dimensions and distortions, making the model tolerant towards image variations.
24. Feature Extraction Classification Image credit: codebasics
25. Some limitations of CNN in Image classification ❏ Requires large datasets to train ❏ Can take a lot of time to train if there are several layers ❏ Can be computing power intensive.
26. A CNN model to identify traffic signs https:/ /bit.ly/traffic_cnn
27. Thank You! Find slide and code link below Demo link: https:/ /bit.ly/traffic_cnn Github https:/ /github.com/waleCloud/solid-fortnight
28. Wale Ayandiran. Software and AI Engineer. Find me at https://walecloud.me Twitter @walecloud