Computer vision final year project ideas and guidelines
Computer vision is the most powerful and captivating sort of AI that trains computers to interpret and understand the visual world. Computer vision uses deep learning models to teach computers how to understand the visual environment so they can readily detect items and react appropriately.
If you are interested in building your final year project or making your career in computer vision this is the article for you to provide some guidelines and project links.
1. Face Recognition
We will learn how to recognize human faces in live video using Python in this project. We'll use python dlib's facial recognition network to create this project. Dlib is a software library that can be used for a variety of purposes. We can create real-world machine learning applications with the dlib toolbox.
2. Semantic and Instance Segmentation on Videos
From this project, we can learn how to use PixelLib in Python to conduct semantic and instance segmentation on videos with just a few lines of code. PixelLib is a library that may be used to segment images and videos. It's a versatile library designed to make image and video segmentation easy to integrate into software systems.
3. Colour Detection & Invisibility Cloak
The goal of this project is to detect color in photographs. It may be used to manipulate and recognize colors in photos and videos. The invisibility cloak is the most well-known project that employs color detection technology. Invisibility in movies is achieved by performing chores on a green screen, but we'll achieve it here by deleting the foreground layer.
4. Human Pose Detection
Pose estimation is an issue in computer vision where we try to figure out what an object's position and orientation are. Detecting keypoint locations that describe the object is usually what this entails. Deep Learning-based Human Pose Estimation with OpenCV is covered in this lesson. You can follow this article to build your own human pose detection project.
5. Vehicle Detection Model
Automated traffic management is one of the most important aspects of a smart city. It utilizes data science skills to create a vehicle identification model that could help with smart traffic management. You can create an automatic car detector and counter model by following this tutorial.
6. Digit Recognition
The task of recognizing the value presented in an image frame using Deep Learning is known as digit recognition. This digit recognition tutorial predicts the numbers written in the MNIST picture dataset using Python, TensorFlow, and Keras.
7. License Plate Detection and Recognition
The project License Plate Detection and Recognition uses detection and OCR techniques to detect the number on a vehicle's license plate. To make your own license plate detection system you can follow this video tutorial or follow this article of another author.
8. Detect Objects in Real-Time
The Viola-Jones algorithm, developed by Paul Viola and Michael Jones, is used to detect objects. Machine learning is used in the aforementioned algorithm. In this article, we'll use one of these Python libraries, OpenCV, to build generic software that can detect any item in a video feed.
9. Real-time Hand Gesture Recognition
Applications for gesture recognition include virtual environment control, sign language translation, robot control, and music composition. From this article, you will learn to create a real-time Hand Gesture Recognizer utilizing the MediaPipe framework and Tensorflow in OpenCV and Python in this machine learning project on Hand Gesture Recognition.
10. Playing Rock, Paper, Scissors with AI
To make a rock, paper, scissors playing system you can follow this article. When it's inside the box, a fine-tuned NASNETMobile model is used to recognize hand signs, and when the model anticipates hand signs, the AI generates its own move at random. The winner of that move is then determined.
Now you can choose from a variety of computer vision project ideas for your senior project. This project will also improve your skills and prepare you for the future workforce.
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