Popular AI technologies like large language models, machine learning, and neural networks seem to be hogging the spotlight lately in the world of AI.
But lurking in the background is the unsung hero of AI… Computer Vision.
Computer vision powers a vast range of AI tech and you’ve undoubtedly witnessed computer vision in action without even realizing it.
In this article, we’ll take a look at 27 computer vision projects with something here for any skill level.
What is Computer Vision?
Computer vision is a field of artificial intelligence that allows computers to interpret and understand the visual world. It allows computers to make sense of visual information similar to how we humans do.
Computer vision encompasses a range of applications beyond mere image and video analysis. It includes object detection and recognition, image classification, motion analysis, and scene reconstruction.
Computer vision techniques are used in a variety of fields such as autonomous vehicles, robotics, healthcare, surveillance, and many others. As the technology becomes more accessible, more industries are adopting AI and computer vision technologies in their products, which makes it a great thing to learn about.
How Does Computer Vision Work?
Computer vision interprets physical and visual data from the real world in a way the computer can make sense of the data.
First and foremost, the data usually undergoes pre-processing to enhance image quality and remove noise, which is crucial for the effectiveness of feature extraction and subsequent analysis.
After pre-processing, data is fed into a machine learning algorithm that detects and extracts the main features, most often using a Convolutional Neural Network (CNN).
The main features can then be described and post-processing and further decision-making can be completed. For example in robotics, the completion of this process may result in a physical action by the robot.
27 Computer Vision Projects
It’s worth mentioning that some of these projects can be relatively simple if using existing pre-trained models and libraries, but they can be extremely challenging if developing these models from scratch. Also, the complexity of a computer vision project can change depending on the scale and the level of accuracy you are aiming for.
1. Color Detection
Color Detection is considered the ‘easiest’ on our list of computer vision projects. It’s a great first project to dip your toes into the world of computer vision algorithms.
Color detection is a computer vision image classification technique used to identify and isolate specific colors within an image or video. By processing each pixel of the media and determining if it falls within a certain range, you can segment out areas of interest based on color.
It’s a great beginner’s computer vision project idea to get a feel for the computer vision workflow. You’ll also discover fundamental tools you’ll use for most computer vision projects.
Color detection is widely used in applications like object tracking, image editing, and real-time video analysis.
Here’s a great guide from TDS on building a color recognizer in Python, one of the best beginner computer vision projects you can start getting your hands dirty with.
2. Image to Sketch
Next on our list of computer vision project ideas is an image-to-sketch project, which is a technique that transforms a standard image into a sketch.
Image-to-sketch can be used in photo editing apps and graphic design. For us at home, it can be a fun project to turn some of your selfies or other photos into a sketch and show off your computer vision skills
This computer vision project typically involves converting the image to grayscale, inverting the colors, applying a blur, and then blending the blurred image with the original grayscale image. The result is an artificial intelligence powered sketch of your input image.
To get started, you’ll need a programming language like Python, and libraries such as OpenCV for image processing – the same tools used for color detections above.
Check out this computer vision project guide that shows you how to generate a pencil sketch from a photo in Python.
3. Image to Cartoon
Image-to-cartoon is another computer vision project idea that utilizes the same basic framework we’ve seen so far.
Image to cartoon is often used in mobile apps, social media filters, and digital art. People have even built online computer vision applications “catoonizing” profile pictures for a small fee.
Unique aspects of this specific computer vision project include stylization to simplify textures and colors, edge enhancement to emphasize outlines, and color reduction to mimic the limited palette typically found in cartoons.
Another challenging task with image-to-cartoon is tuning the algorithm so your output image has artistic appeal, this is where you really get a feel for computer vision and what it can do.
Overall, this is very similar to Project 2 with a few extra steps at the end to get the desired cartoon effects. Learn how to cartoon an image using OpenCV and Python in this computer vision project guide.
4. Face Detection
Next on the list of computer vision project ideas is face detection. As you might have guessed, face detection is a computer vision object detection technique that identifies and locates faces in images and videos.
Face detection is used in various computer vision applications such as autofocus, face tagging in photos, and security systems. Face detection lays the groundwork for facial recognition and other object detection models, which we’ll look at later in the article.
Face detection offers some unique challenges we haven’t seen so far like lighting, facial orientations, discernment, and real-time processing for video.
5. QR Scanner
Next up is one of the more interesting computer vision projects – building a QR code scanner.
A QR scanner is a computer vision application that decodes Quick Response (QR) codes – which are barcodes containing information such as text or URLs.
QR codes are widely used for product identification, tracking packages, advertisements, and business cards for encoding URLs or contact information. You’ll most likely already have a QR scanner built into your phone, try scanning the AI-generated QR code above! Nevertheless, it’s still a fun computer vision project idea.
There are a few unique aspects to this computer vision project idea, one being the robustness against various QR code standards and encoding schemes.
To get started detecting and reading QR codes with computer vision, you’ll need a programming language like Python and a library such as ZBar or Pyzbar for barcode and QR code decoding. You’ll also need images of barcodes and QR codes or a camera to capture them in real time for testing and development.
6. Face Pixelation
Face Pixelation is a computer vision project idea where faces in an image or video are obscured by reducing the resolution of the facial region into large, visible pixels. It’s often used for privacy protection in videos, photos, and surveillance footage to anonymize individuals.
Face pixelation starts building on foundational computer vision skills as we enter intermediate-difficulty computer vision projects.
To get started, you’ll be best off completing Project 4 first where we learned how to recognize faces in images and video feeds. Following this you can apply a pixelation or blurring effect to the regions where faces are detected.
7. Business Card Scanner
Up next in our list of computer vision projects is the business card scanner, which is a computer vision project idea that extracts information from physical business cards.
It’s used for saving contact information without manual entry and is helpful for professionals and businesses to manage their contacts without having to carry around a pocket full of business cards.
The project shows you how to use computer vision techniques to detect information and translate it into useful data.
To build a business card scanner, you’ll need a programming language like Python and libraries such as OpenCV for image processing and Tesseract for Optical Character Recognition (OCR).
Start by capturing an image of the business card, then preprocess the image (e.g., resizing, thresholding), and finally, use OCR to extract text information. Be sure to have a few business cards ready for testing your build.
8. Shadow Removal
Shadow removal is where the difficulty starts to ramp up in our list of computer vision projects as it has some unique aspects in its workings.
Shadow removal is used in object tracking and photo editing, where shadows can impact analysis or general aesthetics.
What makes this project difficult is teaching your model how to differentiate between shadows and dark objects in the reference image.
For this computer vision project idea, you’ll likely employ techniques like background subtraction, color analysis, and illumination adjustment.
There are a bunch of helpful shadow removal assets available online to help with this computer vision project.
9. People Counter
Next on the list of computer vision projects is the people counter or pedestrian detection model. It’s a computer vision application that can automatically count and keep track of the number of people in images or video feeds.
People counters are used for foot traffic analysis and event management for capacity monitoring. People counters don’t worry about identifying individuals and are more focused on the total number of people.
Unique aspects of this project include differentiating between individuals in crowded scenes and tracking people across consecutive frames in video data. Techniques such as object detection, blob analysis, and tracking algorithms are employed.
Handling varying lighting conditions, occlusions, and diverse appearances of individuals are among the challenges that make this project both fun and complex at the same time!
10. Background Removal
A super practical one on our list of computer vision projects is background removal. Background Removal is a computer vision technique that involves separating the foreground subject of an image or video from the background.
This is possibly one of the most common computer vision techniques and commonly used in meeting tools for keeping your home private, in YouTube thumbnails, and in video production for custom backgrounds and effects.
Unique aspects of this specific computer vision project idea include handling various textures, colors, and patterns in both the foreground and background, and ensuring the edges of the foreground object remain crisp after separation.
Deep learning models such as Bodypix or utilizing GrabCut algorithm in OpenCV are common approaches for your background removal computer vision projects.
11. Object Tracking
Object detection and tracking is a computer vision model that involves monitoring the movement of a specific object through a sequence of images or video frames.
It is used in applications such as surveillance, autonomous vehicles, and robotics.
Object detection and tracking are challenging due to changes in object appearance, occlusions, and camera motion.
Multiple algorithms like Kalman filters, mean-shift, or deep learning-based trackers can be employed.
Building on what we learned in the people counter project, object detection and tracking extend capabilities by recognizing multiple unique objects and tracking them over time.
12. Traffic Sign Recognition
Traffic Sign Recognition is a computer vision project idea that identifies and classifies traffic signs and is a critical feature in self-driving vehicles.
Unique aspects of this project include the necessity to accurately recognize a wide variety of signs under different lighting and weather conditions, and at varying distances and angles.
You’ll need to train a deep-learning model using a dataset of traffic signs unique to the region you’re considering.
It typically involves a combination of image classification for sign detection and machine learning or deep learning models (e.g., convolutional neural networks) for the classification of the detected signs.
Traffic Sign Recognition is a project where you really need to test the model under all conditions because if it happened to fail in a real application it could be very dangerous.
13. License Plate Scanner
License Plate Scanner, also known as Automatic License Plate Recognition (ALPR), is a computer vision technique used for identifying and reading vehicle license plates from an input image.
ALPR is widely utilized for traffic management including toll collection, parking management, and applications for monitoring and controlling vehicle movements.
Unique aspects of this project include coping with varying license plate formats, and fonts, and dealing with different lighting conditions, angles, and speeds.
The process typically involves detecting the license plate region, segmenting the characters, and using Optical Character Recognition (OCR) or deep learning algorithms to identify the characters on the plate.
14. Emotion Detection
Emotion Detection is a fun computer vision project idea that involves analyzing facial expressions to deduce an emotional state. To me, a computer that can detect human emotions is pretty awesome.
The project harnesses computer vision techniques, including object detection to locate faces and image classification to categorize facial expressions into distinct emotions.
Emotion detection applications can be found in areas such as human-computer interaction systems, customer feedback analysis, and entertainment.
Ultimately, emotion detection will improve human interaction with AI and give us an overall better experience.
Unique aspects of this project include distinguishing subtle variations in facial expressions and adapting to different lighting conditions and facial features. The process usually involves using facial landmarks for expression analysis and employing machine learning or deep learning models for emotion classification.
15. Age and Gender Estimation
Using similar skills to what we learned with emotion detection, Age and Gender Estimation is one of the increasingly difficult computer vision projects that involves determining a person’s age and gender from their facial features.
This computer vision project leverages object detection to find faces in images or video streams and image classification to categorize the detected faces into age groups and genders.
Age and Gender Estimation is used in targeted advertising, customer analytics, personalized content recommendations, and security systems.
Unique aspects of this project include the challenges in accounting for variations in facial aging across different individuals and dealing with different lighting conditions, poses, and expressions.
16. Plant Detection
Plant Detection is a computer vision technique used to identify and classify plants by analyzing images of plant leaves or stems.
The tech has been used in plant identification apps available in the app store.
It typically involves using image processing to enhance and segment the images, and machine learning or deep learning models for the classification of diseases.
Unique aspects of this project include handling a wide variety of plant species, and dealing with varying lighting conditions.
17. Text Translation
Text Translation in the context of computer vision involves detecting and recognizing text in images or videos and then translating it into a different language.
If you’ve ever been overseas you’ll understand how awesome these tools are. With nothing but your phone, you can read languages across the globe with ease.
Unique aspects of this project include dealing with various fonts, sizes, and orientations of text, as well as handling the complexities of natural language translation.
It typically involves Optical Character Recognition (OCR) to extract text from images, and then using machine translation models to convert the text into the desired language.
18. Pose Detection
Pose detection is a computer vision application that detects the location of a person’s limbs in real-time, giving feedback if necessary.
It’s commonly used in AI fitness apps and home gyms, where computer vision can monitor the subject’s form and keep track of reps and sets.
The unique aspects of this project involve accurately identifying multiple joints and body parts and maintaining robustness across different body types, clothing, and environments.
Real-time feedback is also critical for applications like fitness coaching, meaning your model better be on point!
19. Facial Recognition
Facial Recognition is a computer vision technique that identifies or verifies a person’s identity by analyzing and comparing patterns in their facial features.
It is widely used in security systems, access control, law enforcement, and social media applications. The process involves detecting a face, extracting facial features, and comparing them to a database of known faces, kind of like a fingerprint.
Unique aspects of this project include dealing with variations in facial appearance due to aging, expressions, and lighting.
Facial recognition systems often employ deep learning models, such as convolutional neural networks, for high accuracy and robustness.
20. Food Recognition and Calorie Estimation
One of the more advanced computer vision projects is food recognition and calorie estimation. A bunch of apps have had a go at this, with varying results.
The process typically involves using image recognition to identify the food items and then estimating portion sizes to calculate the calorie content.
Unique aspects of this project include handling a wide variety of food items with different textures and shapes, estimating portion sizes accurately, and accounting for variations in food preparation that might affect calorie content.
Deep learning models are often employed to improve accuracy in food recognition.
21. Document Scanner and Digitizer
Document Scanner and Digitizer is a computer vision application that converts physical documents into digital format.
Document scanners have become a popular mobile app and have reduced the need for expensive physical scanner hardware.
The process involves detecting the document’s edges, correcting the perspective to obtain a flat view, and then using optical character recognition to extract text.
Unique aspects of this project include dealing with various document sizes and formats, correcting distortions and skew, and improving text recognition accuracy even in low-quality images.
This technology is crucial in the digitization of historical documents, book scanning, and data management systems. It’s also just handy, especially with the increase in work-at-home jobs.
22. Handwriting Recognition
Handwriting Recognition is a computer vision project that involves converting handwritten text into digital format. It builds on the document scanner and adds a layer of complexity by adding in highly varying human handwriting.
It’s used in applications such as digitizing handwritten documents, processing forms or checks, and creating searchable archives of historical manuscripts.
The process involves segmenting the handwritten text, recognizing characters or words, and converting them into a digital format.
Unique aspects of this project include dealing with the wide variability in individual handwriting styles, handling cursive writing where characters are joined, and correcting for distortions and noise.
Deep learning, especially recurrent neural networks, are often used to enhance the accuracy of handwriting recognition.
23. Adversarial Attack on Computer Vision Models
Diving into advanced territory, implementing an adversarial attack on computer vision models could be your next computer vision project idea.
An adversarial attack involves creating inputs that are almost identical to normal ones but are designed to fool deep-learning models into making incorrect predictions. This project can expose vulnerabilities in systems like autonomous vehicles or facial recognition.
It requires an in-depth understanding of deep learning models and their workings, as well as proficiency in libraries like TensorFlow or PyTorch.
The primary goal might be generating images that trick a model into misclassifying them, offering an excellent opportunity to learn about the robustness of computer vision models and AI security.
Note: This project demands a high level of responsibility and ethical awareness due to the potential misuse of adversarial attacks.
24. Augmented Reality Filters
Augmented Reality Filters is a fancy name for a Snapchat filter. They overlay digital content onto the real-world view, often through the camera feed of a smartphone.
A unique aspect of this project is the need to create real-time interactions that are immersive, believable, and engaging.
To achieve this, there’s a need for highly optimized computer vision algorithms that can perform facial recognition, object tracking, and image rendering swiftly, even on mobile devices with limited resources.
Additionally, designing creative and visually appealing digital assets is vital to ensure user interest and satisfaction. The combination of technical optimization and creative design makes this an advanced computer vision project.
25. Image Inpainting
Image inpainting is a computer vision model that allows the user to add or remove content from an image. A great example of this is Adobe Firefly.
Inpainting can be used in photo editing for object removal, restoring old or damaged photographs, and video post-processing.
Unique to this project is the requirement for a deep understanding of textures and structures within images to generate visually coherent fill-ins.
Advanced image inpainting often involves some form of neural network, particularly generative adversarial networks, to produce high-quality reconstructions that are almost indistinguishable from the original, unaltered images.
26. 3D Object Reconstruction
3D object reconstruction involves creating three-dimensional models of objects from two-dimensional images or videos.
This computer vision technique finds applications in various fields including, but not limited to, virtual reality, gaming, archaeology, medical imaging, and industrial design.
One of the unique aspects of this project is dealing with the inherent complexity of transforming 2D information into 3D models. This requires sophisticated algorithms to analyze shapes, shadows, textures, and perspectives.
The process often involves combining multiple images, point cloud data processing, and might employ deep learning for intricate structures. Ensuring the accuracy and fidelity of the reconstructed 3D models is a key challenge.
Neuralangelo from Nvidia is a great example of object reconstruction.
27. Sports Analysis
Last but not least on our list of computer vision projects is sports analysis.
Sports analysis using computer vision involves the automatic extraction of relevant information from sports videos and images to evaluate player performance, team strategy, or game dynamics.
It can be used by coaching staff, commentators, and sports analysts for performance enhancement, injury prevention, and audience engagement.
Unique aspects of this project include handling highly dynamic scenes and multiple objects, such as players and balls, in real-time.
Algorithms must be tailored to understand intricate game rules and player movements. Additionally, data accuracy is critical, and integrating additional sensors for spatial analysis or biometrics could add complexity and depth to the insights gathered.
Most recently, sports analysis was used to create AI commentary at Wimbledon.
What Are the Common Examples of Computer Vision Tasks?
Computer vision technology is rapidly advancing and finding applications in a variety of fields and applications. Some common examples encompass:
- Facial recognition in security systems or social media
- Autonomous vehicles employing computer vision tasks for navigation
- Optical character recognition (OCR) in image processing for digitizing text
- Augmented reality apps that use image processing to overlay digital content on real-world images
- Medical imaging analysis deploying deep learning for enhanced diagnosis
- Image and video data content analysis in surveillance systems
- Barcode and QR code scanners in retail
- Smart filters and enhancements in video data and photography apps.
These instances illustrate the versatility and widespread application of computer vision aided by deep learning and image processing and we’re constantly adding to this list.
How Can I Get Started With a Computer Vision Project?
To embark on a computer vision project, first identify a specific problem you aim to solve. This could be your own computer vision project idea or a well-known issue in the field. Collect essential resources such as datasets, tools, and software.
Python remains a dominant language for computer vision tasks, making it hugely valuable to know. Acquaint yourself with libraries like OpenCV, TensorFlow, and PyTorch. A foundational grasp of image processing and machine learning principles is crucial. For educational support, consider enrolling in online courses or following tutorials. Community platforms like Stack Overflow or Reddit can also offer invaluable guidance.
Start with a straightforward computer vision project as outlined in this article, and escalate the complexity as your confidence and skills grow.
What Are Some Computer Vision Jobs?
Computer vision offers a variety of career opportunities in AI, including:
- Computer Vision Engineer
- Data Scientist
- Algorithm Engineer
- Research Scientist
- Machine Learning Engineer with a focus on vision applications
According to Payscale, as of 2021, a Computer Vision Engineer in the United States earns an average salary ranging from $80,000 to $150,000 annually, depending on experience and location. Research Scientists, focusing on computer vision, can earn upwards of $110,000 annually. Machine Learning Engineers with specialization in computer vision have comparable salaries. Additionally, jobs in this domain often come with attractive benefits and opportunities for career growth.
Today we took a look at 27 Computer Vision Projects from beginner level to advanced.
Getting started with computer vision has never been easier with a wide range of online resources and pre-trained models.
As AI continues to integrate into out everyday lives so too will computer vision. The technology serves to improve the way that computers can interact with the world, and the way humans interact with machines.
That’s all from me! Catch-ya 👋