20+ Computer Vision Projects Ideas for Beginners in 2024

On June 10, 2021, Forbes magazine listed 16 Tech Roles That Are Experiencing A Shortage Of Talent. Most of us won’t be surprised to find that out of these sixteen, at least seven of them are related to Artificial Intelligence and Data Science. One such role that the magazine has referred to is AR (Augmented Reality) and MR (Mixed Reality) Architects. According to Gaurav Aggarwal of Avanade Inc., “There’s likely to be a shortage of augmented and mixed reality experience architects. AR and MR are redefining the customer experiences and journey.” In fact, as per International Data Corporation (IDC), worldwide spending on augmented reality and virtual reality will climb up to $72.8 billion in 2024. These numbers essentially suggest that the demand for Computer Vision Engineers is going to rise rapidly soon.

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So, if you are an undergraduate in Computer Science or a Data Science Enthusiast, you should explore Computer Vision Engineer as a career option.

Recommended Reading: Introduction to TensorFlow for Deep Learning

With so many institutions offering knowledge-enriching courses in learning computer vision techniques, it is not challenging to gain most of the skills required to become a computer vision engineer. However, most job seekers struggle when it comes to applying the acquired knowledge in the real world. So, if you are also one of them and are looking for projects in computer vision, then you are on the right page. We have prepared an exciting list of ideas for computer vision projects that you can use to explore and familiarize yourself with the art of performing real-world computer vision tasks.

Computer Vision Projects List

1. Cartoonize an Image
2. Face Detection
3. Similar Images Finder
4. Face Recognition
5. Barcode and QR Code Scanner
6. Face Mask Detection
7. Handwritten Character Recognition using MNIST Dataset
8. Number of People Counter
9. Virtual Invigilator
10. Polyp Segmentation
11. Early Fire Detection System
12. Facial Expression Recognition
13. Text Scanner
14. Number-Plate Reader
15. Projects with Open Images Dataset
16. Deep dreams using CNN
17. Image Retrieval using Content
18. Multi-class Image Classification
19. Neural Image Transfer
20. Traffic Sign Classification
21. Waste Segregation System
22. Anomaly Detection in CCTV Footage
23. Autonomous Drone Navigation
24. Social Media Checker
25. Hand Gesture Recognition

Let us now explore each of these simple computer vision projects in detail.

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Computer Vision Mini Projects

If you are looking for computer vision python projects that are quick to implement and do not take up a lot of time, then the projects in this section are best suited for you.

Computer Vision Project Idea -1 Cartoonize an Image

We all would have at least once downloaded an app that has creative filters and can transform our ordinary images into something more artsy and beautiful. One such filter that we readily come across is the cartoon filter.

If you think the code for creating such cartoon images is complicated, let me tell you it is not. With Python's Image Processing library OpenCV, it is trivial to write a code that can cartoonize images for you. As you know, images are nothing but arrays; we can thus play around with these numbers using mathematical functions to generate creative versions of our images. When cartooning an image, the mathematical functions transform an image into a smooth grayscale image from which edges are detected.  Along with this, another copy of the image is prepared, which has its colors smoothened out. We then overlap the two images to produce the final cartoon version of our original image. This idea may sound complicated at first, but it is a fun and easy computer vision project. As you explore the functions like cvtcolor(), medianBlur(), etc., available in OpenCV, you'll realize that the implementation of computer vision algorithms is straightforward in Python.

Computer Vision Project Idea-2 Face Detection 

All human faces have similar characteristics: for example, all have two eyes, a nose with a bright bridge, a darker skin region around the forehead, etc. Using these features, Paul Viola and Michael Jones created a simple object detection model. They made a cascade classifier that knows how to distinguish between an image that contains a face and an image that does not. You can use this classifier through Python's OpenCV CascadeClassifier() function to detect a face in an image. The exciting add-on to this one of the most simple computer vision machine learning projects is that you can also use it to detect a face in a video using the classifier for each frame.

Computer Vision Project Idea-3 Face Recognition System 

This is another computer vision project that deals with human faces. In 2017, Apple Inc. launched facial recognition technology on its iPhone X. It was a huge deal when Apple introduced this feature, but now it has become a common thing. That's because with exciting libraries being developed each year, it's not difficult anymore to design your facial recognition system. It would be best to create this system as it is one of the most common computer vision projects for beginners. You can use Python for this and use the facial recognition library, which the Python community labels widely as the world's most straightforward facial recognition API for Python and the command line. As a fun exercise, you can even use this computer vision project to identify your favorite celebrities.

Image Processing and Computer Vision Projects for Beginners

This section will cover computer vision project ideas for final year students or novices in the computer vision domain. These easy computer vision projects won’t take your lot of time so don’t think twice before starting to work on them.

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Computer Vision Project Idea-4 Similar Image Finder

While shopping online for our favorite product, we all browse through different shopping sites to find the lowest price. We often use Google Lens for that or Google's Image search. These two perform the task of looking for similar images to your product and list all the websites that have those images. Well, you can build your Similar Image Finder too. By extracting features from the images through a deep learning model like MobileNetV, you can use the KNN algorithm to display the images from an open-source dataset similar to your image. You can then deploy this whole model to solve various kinds of problems. This project initially sounds a bit difficult but don't worry. We have a precisely similar project in our repository that will help you implement this project on computer vision. Check it here: Image Similarity Application with Python, Keras, and Tensorflow.

Computer Vision Project Idea-5 Barcode and QR Code Scanner

This project is another appealing project on computer vision that you can quickly build in Python by installing the pyzbar library along with OpenCV. The pyzbar library has a decode function that locates and extracts information from Barcodes and QR codes in an image. You can use this with OpenCV to display a box around the codes that the decode function has detected. The implementation is as easy as it sounds, so go ahead, start coding for this one.

The projects discussed so far are trivial and easy to deploy. We will now move ahead towards relatively advanced projects that utilize deep learning algorithms.

Computer Vision Project Idea-6 Face Mask Detection

To stop spreading the COVID-19 virus, it has become imperative to wear a mask. While most people thoroughly understand its reason, we still have a few people who don't abide by the protocols. Thus, building a system that can automatically detect who is not wearing a mask is the need of the hour. And, you would have seen many people on LinkedIn posting about a solution to this problem using Computer Vision. It is indeed possible to build such a system with deep learning models. You can download a dataset of images of people with a mask and without a mask. And then use it to train your machine to learn the difference between the two through MobileNetV architecture. This system can then be upgraded for live detection and applied through embedded devices like Raspberry Pi. It will be good to have this project as one of the first raspberry pi computer vision projects that you implement considering the world's current situation in dealing with the pandemic.

The projects on computer vision discussed so far are trivial and easy to deploy. We will now move ahead towards relatively advanced projects that utilize deep learning algorithms.

Fun Computer Vision Projects at Intermediate-level

In this section, you come across projects on computer vision that require a beginner-level expertise in the domain.

Computer Vision Project Idea-7 Medical Image Segmentation

Image: A few samples from MNIST Dataset by Josef Steppan

Polyps are unusual small clumps of cells inside a human body that usually resemble small, flat bumps or tiny mushroomlike stalks. Most of them are harmless and do not require immediate attention. However, they can evolve into cancer, and thus advised to have all kinds of polyps removed from a patient's body. It is sometimes difficult for a human eye to detect polyps from the colonoscopic images, and this is where the vision of a computer can be of great help. Using the CVC Clinic Database containing frames extracted from colonoscopy video, you can build a deep learning model using Python libraries like Pytorch, OpenCV, sci-kit-learn, pandas, NumPy albumentations, etc. You can use the model Unet++ for this as it is widely used for Medical Science purposes. Check the full solution for this computer vision project idea here: Medical Segmentation using Deep Learning Project.

Advanced Computer Vision Projects

This section will cover interesting computer vision projects that are slightly more challenging than the previous ones.

Computer Vision Project Idea-10 Number-Plate Reader

Often we have seen car riders committing an offense and getting away with it by speeding up. That is why agencies responsible for crime control are taking measures to stop this. They are gradually relying on computer vision solutions that can read the number plate of vehicles and assist them in penalizing offenders. Thus, it'd be a great idea to build a project around this. It would help if you looked for countries with a standard set for number-plate designs and then form your dataset using Google Image Search. Of course, you will require a digit recognizer for its implementation, and you can start with the project from our repository- Digit Recogniser using Python. Don't forget to go through its second part to have a complete solution for the problem.

Computer Vision Project Idea-11 Projects with Open-Source Images Dataset

We have detailed so many exciting projects for you, but if you have your idea and want to implement it, here is an idea for an open-source computer vision project. There are tons of image datasets available online that you can download to transform your computer vision project idea into a reality. A few of them are COCO Dataset, ImageNet, and Open Images, etc. These datasets contain Images and a corresponding label with them that denotes what object the image has. You can use the existing images in these datasets and can even add a few classes on your own through Transfer Learning to train your deep learning model.

Computer Vision Project Idea -12 DeepDream using CNNs

You can utilize Convolutional Neural Networks (CNNs) in this project to create dream-like hallucinatory pictures. Here’s how to proceed with this project-

• Use Python and Keras Functional API along with Amazon Web Services (AWS) for this exciting project. Along with CNN, use bat-country, an easy-to-use, highly extendible, lightweight Python program.

• Install it from GitHub or use pip to install it: pip install bat-country or pip install --upgrade bat-country.

• Apart from these, you also need a functional Caffe installation.

• Begin with a picture and process it at different octave scales.

• Then, increase the activation of complete layer sets for each octave, and blend the results to create "psychedelic" effects.

• The most psychedelic visuals are produced by CNN trained on the ImageNet dataset.

Computer Vision Project Idea -13 Image Retrieval using Content

Content-based image retrieval engines compare photos for similarity using extracted feature vectors and a reference image. To create a feature vector, start with an image and apply an image descriptor. The image attributes can then be saved in a database. When you need to compare two new photos, just compare the new image's feature vector to that of the current feature vector database and return the feature vector with the closest distance measures. You can implement the code in Python and also use Tensorflow for feature extraction and Numpy for distance calculation. You can use the CBIR dataset from Kaggle. After retrieving all of the images, you can use CNN’s VGG-16 architecture for extracting required features from the latter and save them in a .npy format for further analysis.

Computer Vision Project Idea -14 Neural Image Transfer

The neural style transfer method applies the style of one picture to the content of another. The Loss function is the essential component in choosing a Neural Image Transfer. So, the Loss function should be your primary emphasis.

You can calculate the loss function with the help of the Gram matrix. Also, when it comes to packages- for neural networks, you may use Keras and Tensorflow, and for data processing, you can use Numpy. You'll need to run the primary picture through a classification CNN; you may use Keras' VGG19 network, a neural network trained on the ImageNet dataset.

Best Computer Vision Projects in Python

Discover exciting applications of Python in these projects on Computer Vision where we delve into practical uses of advanced technology with ease.

Computer Vision Project Idea-15 Handwritten Character Recognition using MNIST Dataset

Image: A few samples from MNIST Dataset by Josef Steppan

MNIST ("Modified National Institute of Standards and Technology") is a very popular dataset among the community members of Computer Vision (CV) Engineers. It was developed by Yann LeCunn, Corinna Cortes, and Christopher J.C. Burges and released in 1999. It contains images of handwritten digits. The reason for its popularity is that CV engineers use it for training and testing their classification algorithms. The dataset is easy to access, and you can download it from here. After thoroughly going through the details of the dataset, you can attempt to build a handwritten character recognition system. You can use Convolutional Neural Network (CNN) architectures like LeNet-5, VGG-16, etc., for classification. If you want step-by-step guidance on how to build this interesting computer vision project, we have the perfect link for you. Please take a look at our beginner-friendly computer vision project: Digit Recognizer Part 1 and Part 2 that will guide you through the practical implementation of this Project in Python using TensorFlow.

Computer Vision Project Idea-16 People Counting Solution

You enter a shopping mart and see cameras everywhere. The initial idea of installing these cameras was to prevent thieves from stealing things. But, now, it has become a tool to analyze the shopping pattern of customers. For a business to grow, it is vital that it invests time in analyzing its customers' behavior. This analysis can help them in tracking which days specific discounts should go live. Thus, as a beginner, you can try to build a computer vision-based people counter. First, all you have to do is detect whether the frame contains a human being or not and then increase the counter for each unique human being the system detects. This project is a step ahead of the facial recognition system that we have already discussed. For this project, you should explore libraries like NumPy, OpenCV, dlib, and imutils. Do take care that this project requires you to do object tracking. So, while building a solution to this, make sure you use the object detection techniques for each video frame.

Computer Vision Project Idea-17 Virtual Proctor

This project is another application of Computer Vision that has recently gained popularity because of COVID-19. Because of the virus, the world came to a standstill, but the Computer Vision Engineers made sure that companies' hiring process doesn't come to a halt. They designed, what we'd like to call, a virtual invigilator that allows a job seeker to sit for written tests and makes sure they do not cheat. Educational institutions have also used this project to conduct examinations remotely.

The approach to this CV project can be divided into four parts:

1. Tracking the gaze of the person who is being proctored.

2. Detecting whether the mouth of the person is closed or open.

3. Counting the number of people on the screen.

4. Detecting the presence of a mobile phone.

All these four tasks will require you to understand the object detection models like SSD, YOLO, Faster R-CNN. So, go ahead, attempt to build this project on computer vision today.

Python Computer Vision Projects with Source Code

The projects in this section are best suited for individuals who are looking for for a practical and accessible introduction to the exciting field of computer vision.

Computer Vision Project Idea -18 Traffic Sign Classification

To address complexities for any image data, CNN is the preferable approach. Any data that has spatial links are suitable for CNN. The project's purpose is to show you how to create and train a Convolutional Neural Network. Keras can be used as the frontend, with Tensorflow 2 as the backend. Use the German Traffic Sign Recognition Benchmark (GTSRB) from Kaggle to train your own customized traffic sign classifier. For resizing images within the dataset, you can use the resize function in the cv2 package from OpenCV, which has various interpolation techniques. You can also check out the source code on GitHub. If you are looking for computer vision projects on github, then you must not skip this project.

Computer Vision Project Idea -19 Multi-Class Image Classification

Multi-Class Classification problems are the types of problems in machine learning where the target variable has more than two options for its value. For example, consider a dataset of a credit card company, and let us assume one is trying to predict whether a loan applicant will default or not. If we use the three variables to represent the possible outcomes of ‘yes’, ‘no’, and ‘maybe’, then this problem will be a multi-class classification problem. Consider working with this project if you want to explore how such problems are solved when working with images.

Project Objective: Solving a multi-class image classification problem in Python using deep learning algorithms.

Learnings from the Project: This project will teach you how to solve multi-class image classification problems from scratch. You will get to closely look at the architecture of a CNN which means you will be exploring the different activation functions used in different layers. These activation functions will include Step, Sigmoid, ReLU, leaky ReLU. The project will guide you to different steps involved in building the architecture of a CNN like Pooling, Flattening, etc. The optimization of weights in a CNN using gradient descent and stochastic gradient descent will also be discussed.

Tech Stack: Language - Python

Libraries - NumPy, matplotlib, TensorFlow, cv2

Access the full solution to the project: Build a Multi-Class Image Classification Model Python using CNN

Computer Vision Project Idea-20 Early Fire Detection System

'Fire Fire everywhere, not a tree alive!'. This quote perfectly fits the Amazon rainforest fires, which became a global concern in 2019. It is crucial to prevent such forest fires if we wish to fight against climate change. And a solution for this can be provided by deep learning algorithms. One can use the RGB model of colors to identify fire and smoke regions in an image. You can train a model like MRCNN for this purpose. You can then implement the whole idea on each frame of a live video to get real-time feedback from the system about the presence of flaming regions. Access the full solution to this computer vision project idea here: Using Mask-RCNN with TensorFlow for Image Segmentation.

Computer Vision AI Projects

In this section, we will showcase some cool computer vision projects and explore how they are used across industries for building exciting systems.

Computer Vision Project Idea -21 Waste Segregation System

The project involves capturing images of waste materials using a camera, which are then processed using computer vision and deep learning algorithms to classify the waste into different categories such as organic, recyclable, and non-recyclable waste. For image classification, you can either use a dataset such as the COCO dataset or build your own dataset.

Once the system learns how to label the waste correctly, it can sort the waste materials based on their category using automated mechanisms such as conveyor belts or robotic arms. This technology has the potential to significantly improve waste management systems and reduce the amount of waste that ends up in landfills, contributing to a cleaner and more sustainable environment.

Computer Vision Project Idea -22 Anomaly Detection in CCTV Footage

Computer vision can be used for anomaly detection in CCTV footage, where it analyzes the visual data to detect unusual events or suspicious activities. For example, it can be used to detect people loitering or carrying suspicious objects.

To build the solution for this project, we suggest you first work on the Human Activity Recognition Project that we discussed in our blog on data science projects. Create a sample dataset and label more advanced human activities such as loitering. Train the deep learning algorithms on that dataset, you can use transfer learning techniques for quicker implementation.

You can use this system to indicate a potential threat or anomaly. Security personnel can be alerted to potential threats that will help them take appropriate action to prevent harm. The system can also be used to identify patterns and trends in security footage, providing valuable insights into areas that may require additional security measures.

Computer Vision Project Idea -23 Autonomous Drone Navigation

Computer vision can be used to enable autonomous drone navigation, allowing drones to navigate their environment and avoid obstacles using visual input from a camera. 

The project solution will involve processing the visual data and creating a 3D map of the drone's surroundings with the help of object detection using deep learning algorithms (CNNs). By using this map, the drone can identify obstacles and plan its path accordingly.

This solution can be used for various applications, such as aerial mapping, inspection, and monitoring, as it enables drones to navigate complex and dynamic environments without the need for human intervention.

Computer Vision Project Idea -24 Social Distancing Checker

Building a computer vision project on a social distancing checker involves using computer vision algorithms to detect and monitor people's movements in public spaces. The system must be able to ensure social distancing rules are obeyed by raising an alarm if people are standing too close to one another and request them to maintain a safe distance. 

The project can be implemented using object detection algorithms like YOLO or Faster R-CNN and can be trained on datasets like COCO or Open Images. Additionally, real-time video processing techniques can be used to enable the system to operate in real-time, making it practical for use in public spaces to help enforce social distancing rules.

Computer Vision Project Idea -25 Hand Gesture Recognition

If you are looking for fun computer vision projects, then this project is a must try. In this project, you will build a system that will recognise hand gesture using computer vision techniques. This project can be applied in many real-world applications like sign language translation, virtual reality, and human-computer interaction systems.

To implement this project solution, you will have to train a deep learning algorithm on a dataset that can be collected using a camera or sensor that captures images of the hand gesture. You can use Convolutional Neural Networks like VGG-16 to perform image classification.

Computer Vision Open Source Projects

This section will introduce you to three captivating Computer Vision Open Source Projects, offering a hands-on experience in the world of visual intelligence.

VisioSuite

Viso Suite is a versatile computer vision library designed for image processing, feature extraction, and pattern recognition. It's widely used in applications requiring robust visual analysis. You can enhance Viso Suite's capabilities by contributing to its integration project. Whether you're adept at code optimization or have ideas for new features, your contributions can shape the future of this powerful library.

DeepFace

DeepFace is a facial recognition system that uses deep learning to analyze and identify faces in images. It has applications in security, user authentication, and social media. Join the DeepFace implementation project to improve facial recognition accuracy and expand its functionalities. Your expertise in deep learning algorithms or experience in training models can significantly contribute to the project's success.

YOLO

YOLO (You Only Look Once) is a real-time object detection system that excels in identifying objects in images and videos swiftly. It's widely utilized in autonomous vehicles, surveillance, and image analysis. Contribute to the YOLO project to enhance its object detection capabilities. Whether you specialize in optimizing algorithms or have a knack for improving model accuracy, your contributions can play a crucial role in advancing real-time object detection.

Now that we have explored some interesting computer vision projects, let's delve into the role of a computer vision engineer and how they contribute to the development of such projects.

Role of A Computer Vision Engineer

If you want to become a Computer Vision Engineer, you should have a clear idea of what kind of responsibilities the role demands. Here is a list of them:

1. Use Deep learning models on the company's data to derive solutions that promote business growth.
2. Leverage machine learning libraries in Python like Pandas, Numpy, Keras, PyTorch, TensorFlow to apply Deep learning and Natural Language Processing on huge amounts of data.
3. Explore and analyze images through Image Processing Techniques and come up with relevant conclusions.
4. Optimize the implementation of the machine learning and deep learning algorithms for tasks like Image Classification, Object Recognition, and reduce processing time.
5. Actively participate in team meetings with Data Scientists and Machine Learning Engineers to present insightful results timely and neatly.
6. Deploy existing computer vision models after optimization to meet customer requirements and maintain them for future use.

And in case you have no idea where to begin, don't worry, we’ve got you covered with some fun computer vision projects to practice and master your skills to become a computer vision engineer.  Here’s the learning path to follow to achieve your dream of becoming a Computer Vision Engineer.

Top Skills Required for a Computer Vision Engineer Role

First things first, you need to know the top skills required by the market to prepare yourself before applying for a job. So, here is the list:

1. Knowledge of Image Processing Techniques, Image Recognition, Object Detection, and Visual Recognition.
2. Understanding of Deep Learning Neural Network architectures (ANN, CNN, RNN, Transformers, Autoencoders) and their applications in solving Computer Vision problems.
3. Strong ability to code in programming languages like R/Python/Matlab.
4. Deep understanding of Data Structures and algorithms.
5. Strong foundation in Mathematics and Statistics.
6. Must be able to draw insightful conclusions from the dataset and present them in an organized manner.
7. Good communication skills.
8. Prior experience in solving complex real-world problems in computer vision topics using machine learning and deep learning algorithms.
9. A strong curiosity to dig deeper into computer vision research topics.

Besides this, they also expect you to hold a degree in Computer-Science/Mathematics/Statistics or any related field.

Explore the World of Computer Vision with ProjectPro's Enterprise-Grade Computer Vision Projects

Of course, we understand that reaching a level where you can build solutions for all the above computer vision project ideas will be time-consuming, and you will require good guidance. Therefore, to help you with saving time and reaching your goal in a relatively short time, we have prepared insightful and engaging content on solved end-to-end Data Science projects for you. Each of these solved projects comes with a reusable code, explanatory videos, a downloadable dataset, and 24x7 technical support. So, get access to these solved end-to-end data science and machine projects and get a step closer to your goal of becoming a computer vision engineer.

FAQs on Computer Vision Projects

1. How to handle image preprocessing and data augmentation in computer vision projects?

Image preprocessing and data augmentation are essential steps in computer vision projects as they improve model performance and reduce overfitting. Several deep learning frameworks provide built-in functions for image preprocessing and data augmentation, including TensorFlow, PyTorch, and Keras.

2. How do you evaluate the performance of a computer vision model?

The choice of evaluation metric depends on the specific task and the project's goals. One can use the classification metrics such as accuracy, precision, recall, and F1 score in case of a project that aims to perform classification tasks. For object detection based applications metrics such as mean average precision (mAP) and intersection over union (IoU), are used.

3. What are some common challenges in computer vision projects, and how can one approach them?

Common challenges in computer vision projects include limited availability of annotated training data, hardware limitations, and overfitting. These challenges can be approached by using transfer learning to leverage pre-trained models, implementing data augmentation techniques to increase the size of the training dataset, using hardware accelerators to improve model performance, and applying regularization techniques to reduce overfitting.

About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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