Top Computer Vision Projects to Sharpen Your Skills and Build Your Computer Vision Portfolio

Computer vision is a branch of artificial intelligence and machine learning. It involves automating computer systems to elicit useful information from digital images and videos. Professionals use deep learning models and machine learning algorithms to imitate natural thought processes in identifying, classifying, and analyzing objects at pixel levels through computer vision. 

Computer vision also includes enabling systems to recommend actions based on perceptions from its visual system. This exciting field can be the right career for you if you’re interested in exploring the creative dimensions of artificial intelligence. To prove your competence to potential employers, you need a well-polished portfolio. Read on to find out how to build one.

5 Skills That Computer Vision Projects Can Help You Practice

While structuring an impressive project portfolio may be challenging, it also tells employers that you have the technical skills needed for the job. Computer vision helps you build these valuable skills so you can improve your chances of securing standard jobs in the field.

• Programming. Most computer vision projects involve Python programming because of how amenable this language is. This gives you ample opportunity to build your Python skills. You’ll also work with other languages such as JavaScript and C++ programming. 
• Image processing. Image processing is a computer vision method used to improve image quality or gather information from pixel data in an image using an algorithm. Most of the projects you’ll work on will enable you to develop this skill. 
• Machine learning. Most computer vision projects rely on machine learning models in automating system processes in carrying out their tasks. Machine learning is an exciting and broad field. Knowledge of its algorithms can help improve your skillset. 
• Deep learning. Deep learning is a complex field that relies on designing applications that imitate human thought patterns. Working on computer vision projects will expose you to deep learning models and techniques that boost your portfolio. 
• Designing computer graphics. You’ll work on computer vision projects that require you to design graphical user interfaces, which would help you improve your knowledge of computer graphics. 

Best Computer Vision Project Ideas for Beginners 

If you’re thinking about starting a career in computer vision or you’re simply fascinated with the concept and would like to explore your options, you should consider trying out a few of these beginner project ideas.

Primary Colors Detection Application

• Computer Vision Skills Practiced: Programming, image processing

Color detection is a popular project in computer vision. In this project, you’ll practice app creation using Python programming to design an application that helps you automatically identify colors in an image. To create a primary color detection app you’re going to need a dataset that contains primary color values of red, yellow, and blue.

This project requires knowledge of Python programming to write the script for the application and Python-computer vision libraries consisting of OpenCV for real-time image processing and pandas for image manipulation and data analysis. You would need to install them, along with your CSV file that contains your color dataset.

Handwritten Digit Detection App

• Computer Vision Skills Practiced: Programming, deep learning

The handwritten digits detection project involves programming a computer system to recognize handwritten text. This project requires the use of an aspect of deep learning called the convolutional neural networks. You’ll create a graphical user interface application that allows you to draw texts, and it will be interpreted automatically.

To create this project, you need to have a foundational knowledge of Python programming to write the API and the front end languages to design your user interface. You also need to have knowledge of Python libraries for deep learning, such as Keras to acquire a Python interface for artificial neural networks and Tkinter for creating the GUI.

Car Edge Detection Tool 

• Computer Vision Skills Practiced: Programming, image processing

If you’re interested in exploring more simple hacks, the edge detection project is an exciting project idea for beginners. In this project, you’ll learn to identify the shapes or edges of objects in an image by processing the pixel values to find underlying differences. The difference in the pixel values helps you identify the edges in the picture. 

Instead of creating an application that identifies different random objects, you can create one that targets different types of car edges and classify them accordingly. You’ll need a matrix program like Kernel to identify the edge automatically. This project will help you improve your skills in image processing and object classification.

Portrait Image to Sketch App

• Computer Vision Skills Practiced: Programming, image processing

There are several projects you can do with computer vision libraries. One of them is the image to sketch project. This project is an ideal task for building your portfolio, even as a beginner. To get started, you would need to have knowledge of Python programming and OpenCV for processing images. 

This project involves creating an application that you can use to convert photos into sketches. You can add a creative edge to your project by creating an application that only converts portrait images into pencil sketches. Python is an easy programming language to learn, but you can increase your knowledge through online Python courses. 

Pedestrian Crossing Detector 

• Computer Vision Skills Practiced: Programming, image processing 

Using the OpenCV library, you can write a program to detect road crossing lines for pedestrians. This task is a simple computer vision project that explores aspects of object detection using Python. The result of this project can be used in road lane coordination to prevent accidents for self-driving cars. 

To get started, you use a tool like Numpy to mask aspects of the pixels in the image that don’t have white markings. Then, you can proceed to deduce the pedestrian lanes from the white markings. To do this, you would need a mathematical technique like hough transform to detect imperfect instances in the white markings frame. 

Best Intermediate Computer Vision Project Ideas 

Below you’ll find a list of engaging intermediate-level computer vision projects to boost your learning experience and increase your chances of landing good jobs in this field. These project ideas require you to have some technical familiarity with machine learning and artificial intelligence. 

Face Recognition App

• Computer Vision Skills Practiced: Machine learning, programming

Beyond detecting human objects, we can train our systems to recognize faces. The face recognition project can reveal the identity of facial objects in an image using a set of algorithms. You can use this project to identify pictures for security reasons such as signing in to your device or office, assessing your financial data, or mass surveillance. 

To create this application using OpenCV, you first must create a face detection program. Then you extract the features of the objects in the image to make out faces. To remove facial features in an image, you would use a neural network to convert faces into vectors called face embeddings. The vector contains the essential elements of the analyzed face.

Traffic Light Detection

• Computer Vision Skills Practiced: Machine learning, programming

With computer vision technologies, you can use an object detection model to analyze, classify and provide feedback on images. In this project, the focus is on designing a traffic light detection model that analyzes traffic light video images to identify and classify traffic lights according to their color categories. 

For this project, the traffic lights will be grouped into separate classes using an object deception API. You can use the TensorFlow object detection API, which allows you to configure the model. Knowledge of Python programming and TensorFlow is a requirement for this project. You would also need a traffic image dataset to create your object detection model.

Hashtag Image Caption Generator

• Computer Vision Skills Practiced: Machine learning, image processing

Image captioning is a process of assigning text descriptions to an image. You can train your computer system to generate hashtag captions for photos taken in real-time. If properly designed, you can use this tool commercially for social media uploads, and it would serve as an ideal addition to your portfolio. 

To create this application, you will need a visual attention-based model architecture to target specific aspects of an image and base the predictive captioning. You will train the model using a caption dataset that will form the caption generator library.

Real-Time Human Image Contouring

• Computer Vision Skills Practiced: Programming 

This intermediate project will allow you to explore the different types of contouring techniques. Image contouring is the process of analyzing the outlines of objects in an image to deduce the shape. Imaging contouring can be used in industries like defense and security to detect unfamiliar patterns in images. 

Using OpenCV, you can create a program to detect humans in an image by outlining the structures of the objects in the picture. To detect image contours in an image, you have to convert the picture to a grayscale image. Then use a canny edge detection or binary thresholding to segment the image’s objects before drawing the contours. 

Android Document Scanner 

• Computer Vision Skills Practiced: Machine learning, programming

Having a personal scanner can simplify many things for you and this mobile Android document scanner project is the perfect starting point. It can help you protect your document contents from unwanted access. It will also allow you to take pictures of documents and quickly scan them on the go. 

In this project, you’ll create an interactive Android document scanner that makes it easy to share and edit texts in your document. First, you would need an open-source service like OCR to convert scanned document images to editable text formats, a machine learning text recognition tool. Once you secure these, you can create your project with the Android studio.

Advanced Computer Vision Project Ideas

An advanced-level computer vision project requires in-depth knowledge of deep learning algorithms and convolutional neural networks so you can create advanced projects that address real-world computer vision problems while promoting your professional image. Below are some project ideas to help you get started.

Dancing Gesture Recognition 

• Computer Vision Skills Practiced: Deep learning, machine learning, programming

This computer vision task is quite similar to the hand gesture recognition project, except that instead of training your computer system to identify a static raised hand, you’ll program it to recognize dance movements in a real-time image or video capturing. This project requires knowledge of programming and TensorFlow. 

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You also need to understand the mechanics of neural networks to attempt it. Familiarity with the hand gesture recognition project will enable you to have a conceptual grasp of this project to proceed with trying it. You would also need knowledge of OpenCV so you can conduct real-time image processing. 

Speech Emotion Recognition Model

• Computer Vision Skills Practiced: Deep learning, programming 

You can train a computer system to recognize speech emotions using Python libraries such as Librosa and Soundfile and machine learning algorithms to create this model. Librosa is a Python library used to process and analyze audio and music. 

To begin, you need an audio and music sound dataset to train your model. This training dataset will teach your algorithm to recognize emotions from speech sounds. Additionally, knowledge of MLPclassifier is required to build the model. MLPclassifier is a binary classification neural network model.

Content-Style Image Transformation App

• Computer Vision Skills Practiced: Deep learning, designing computer graphics

Using the neural style transfer technique in deep learning, you can implement the styles from any image to a content image. This project is ideal for designers and artists who want to imitate professional creations. With this tool, you can visually transform a sketch you made with a painting style from another image. 

For this project, you need to use the trained convolutional model for image classification and Keras programming for transfer learning. You also have to define the loss function using a metric like per pixel losses or perpetual losses to compare the difference between the input pixel values and output values and calculate the number of errors in the pixel images.

Animal Semantic Segmentation App

• Computer Vision Skills Practiced: Deep learning

Semantic segmentation involves assigning object classes to each object detected in an image. The animal semantic segmentation app can be a valuable tool for identifying different animals in a real-time image, especially educational or security surveillance. Such tools can reduce the rate of accidental animal maiming. 

To proceed, you will need a deep learning model like Google’s DeepLab. DeepLab is an open-source semantics segmentation architecture used for object labeling. This model will extract pixel data from the image and give out essential features of the picture. A good understanding of DeepLab is necessary to achieve success with this project.

Inanimate Object Tracking App 

• Computer Vision Skills Practiced: Programming

Object tracking in computer vision is assigning motion identifications to detected objects in an image or video. This makes it easy for the viewer to monitor the things identified. This project aims to create an application that allows you to track only inanimate objects in an image using an algorithm called centroid tracking, which assigns each detected object an ID. 

To create this application, you need to know algorithmic centroid computations and Python programming techniques. Your knowledge of Python will enable you to write the Python script, which you will use to run the inanimate object tracker in a video. Previous practice with object detection in OpenCV is required. 

Computer Vision Starter Project Templates

Starter projects provide you with a framework to base your project on, but you might want some basic templates to create new projects yourself. The computer vision project templates listed below will provide you with the prerequisites for designing engaging real-world applications and devices. 

• Computer vision Python-TensorFlow Starter Template. This is a template for creating computer vision-related projects using Python and TensorFlow.
• Hackathon-Capture Image Computer Vision Template. You can use this template to create image recognition applications using Python programming. 
• Pytorch Deep Learning Computer Vision Template. You can use this project template to conduct deep learning computer vision projects using PyTorch.
• C++ Computer Perception and Graphics Template. This project template can be used to design computer vision applications. It comes with configurations of external libraries such as OpenCV.
• C++ Computer Vision Applications Templates. This template can be used to develop computer vision applications using the C++ programming language.

Next Steps: Start Organizing Your Computer Vision Portfolio

Building your computer vision portfolio should be as fun as it is taxing. You’ll work on projects that will enable you to explore aspects of machine learning and artificial intelligence. But before you hop on the project train, there are several factors you should consider to ensure your portfolio comes out looking professional. 

Create a Computer Vision Portfolio Plan

This is the first step to ensuring you achieve success with your computer vision portfolio. Your portfolio should have a trajectory. It should emphasize your computer vision skill progression, from the beginner level to the advanced level. The only way to control this progression is to build a portfolio plan that outlines each of your projects.

Ensure Your Innovative Portfolio is Market-Led

All of your efforts and knowledge won’t make a difference to recruiters if you can’t tailor your portfolio to suit their needs. Look out for the latest trends in the computer vision sphere and attempt project ideas that are gaining the most significant attention or seem challenging but innovative. 

Use Resources to Improve Your Portfolio 

There are several computer vision resources out there for you to take advantage of. Attend computer vision training courses so you can efficiently build projects in this field. Join forums and network with like-minded professionals. Take advantage of starter templates and training datasets to improve your project quality.

Computer Vision Projects FAQ