“Artificial intelligence is the science of making machines do things that would require intelligence if done by men.” — Marvin Minsky, co-founder of MIT’s Artificial Intelligence laboratory.

The quote above nicely sums up the beauty of Artificial Intelligence (AI). Using AI for automating simple tasks allows humans to invest in solving more challenging problems. That is why we all are witnessing AI gaining much traction despite the technology being in its infancy. One can easily affirm this by looking at Gartner’s recent survey, which revealed that by the end of 2024, 75% of organizations would shift from piloting to operationalizing AI.

Artificial intelligence techniques like machine learning, deep learning, natural language processing, etc., allow their users to draw insightful conclusions from the data that wouldn’t have been revealed otherwise. They also offer individuals to make predictions about specific parameters, thereby preparing them for the future. And, please do not think of the dataset as a collection of numbers. Gone are the days when that used to be the case. With the advent of technological advancements in AI, extracting information from images and texts has become possible.

The branch of AI that deals with harnessing the potential of data in the form of images and videos is called Computer Vision. There are many exciting applications of Computer Vision (CV), and in this blog, we are going to list AI project ideas that a CV enthusiast can work on. The project ideas have been split into categories mentioned below so you can smoothly browse through them as per your experience in the industry.

  • AI Projects in Computer Vision for Beginners
  • AI Projects in Computer Vision for Intermediate Professionals
  • Challenging AI Projects in Computer Vision for Experts

AI Projects in Computer Vision for Beginners

1) Face Recognition Application

Face Recognition is a fun computer-vision-based application that most beginners enjoy building. Just think of it, an application that sees your picture and identifies you with your name, sounds cool right? Creating such an application is not as difficult as you may think with so many computer vision libraries.

Solution Approach: Building a face-recognition system in Python is quite simple using Haar Cascade Classifiers. It is a pre-trained model that can detect the presence of a face in a given image. You can use this model to locate a face in an image and then use the KNN machine learning algorithm to estimate how closely it matches another face.

Dataset: Use the Yale Face Database for this project that has 165 images in a grayscale of 15 persons.

Use-Case: Face Recognition is widely used as a security feature, for example, on the lock screen of mobile phones to prevent random individuals from unlocking it.

2) Mask Detection

With China closing its schools and cancelling its flights again to combat a recent surge in coronavirus cases, citizens worldwide feel alarmed. We all know by now that maintaining a physical distance of at least 2 metres and wearing masks are the two primary steps that we can take to control the spread of the virus. Yet we see so many people not wearing masks when in public places. A solution to this problem can be to use CV to build a system that can detect people who are not wearing masks.

Solution Approach: Use a CNN model like ImageNet and train it to learn the difference between faces with a mask on them and faces that don’t. After a decent accuracy has been achieved, the next step will be to detect the facial features in the given image. Lastly, apply the model to test the presence of a mask.

Dataset: You can use the COVID-19 images dataset by Prajna Bhandary for this project that has 690 images of people wearing masks and 686 images of people without masks.

Use-Case: This model can be deployed at public places to ensure people who are not wearing masks are fined.

3) Dog and Cat Classification Project

The goal of this project is to learn Image classification using computer vision. It is a fun computer vision project idea for beginners where they will train a deep learning algorithm to distinguish between the images of dogs and cats.

Solution Approach: For this problem, you can build a simple CNN model from scratch using TensorFlow and Keras in Python and train it to learn the features of cats and dogs. As an alternative, you can also use a simple CNN model like VGG-16 to distinguish between the two animals automatically.

Dataset: Dogs vs. Cats Dataset on Kaggle

Use-Case: This project idea is best to learn how convolutional neural networks (CNN) models are built from scratch using TensorFlow and Keras library in Python.

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