Top 10 AI Project Ideas in Computer Vision

Top 10 AI Project Ideas in Computer Vision

This quote sums up the beauty and power of Artificial Intelligence. Humans can use AI to automate simple tasks and invest in more difficult problems. This is why AI is gaining much traction even though it’s still in its infancy. This is easily confirmed by Gartner’s recent survey, which showed that 75% of AI-focused organizations would move from operating to piloting by 2024.

Machine learning, deep learning, and natural language processing are all examples of artificial intelligence that allow users to draw inductive conclusions from data that would not have been possible otherwise. These techniques allow individuals to make predictions about certain parameters and prepare them for the future. Please don’t think of the dataset simply as a collection. This is no longer the case. It is now possible to extract information from images or texts thanks to technological advances in AI.

The branch of AI that focuses on harnessing the data potential in the form of images and videos is called Computer Vision. Computer Vision (CV) has many interesting applications. This blog will list some AI project ideas that any CV enthusiast could work on.

Top 10 AI Project Ideas in Computer Vision

1. Face Recognition Application

Face recognition is a fun computer-vision-based application that most beginners enjoy making. It’s a simple application that recognizes your face and matches it 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: Using Haar Cascade Classifiers, it is easy to build a face recognition system in Python. This pre-trained model can detect the presence or absence of a face within an image. This model can be used to find a face within an image. The KNN machine-learning algorithm can then be used to determine how close it is to another face.

Dataset: This project uses the Yale Face Database, which has 165 images in grayscale for 15 people.

Use-Case: Face Recognition is used widely as a security feature on mobile phones’ lock screens to prevent uninvited individuals from unlocking them.

Also read: Top 5 Computer Vision Applications

2. Mask Detection

Citizens around the world are alarmed at China’s decision to close schools and cancel flights in order to stem a recent rise in coronavirus cases. As we all know that maintaining a physical distance of at least two meters and wearing masks are the two first steps we can take to control the spread of the virus. We see many people who don’t wear masks in public. This problem can be solved by using a CV to create a system that can detect those who aren’t wearing masks.

Solution Approach: Using a CNN model such as ImageNet and Train. It will help you distinguish between faces with and without a mask. Once you have achieved a good level of accuracy, the next step is to identify the facial features within the image. Finally, use the model to verify the presence of a face mask.

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

Use-Case: This model can also be used in public places to make sure that people who aren’t wearing masks are punished.

3. Dog and Cat Classification Project

This project aims to teach computer vision Image classification. This is a great computer vision project for beginners. They will use deep learning algorithms to distinguish between images of cats and dogs.

Solution Approach: To solve this problem, you can create a simple CNN model using TensorFlow in Python and Keras. Then train it to recognize the characteristics of cats and dogs. You can also use VGG-16, a simple CNN model to automatically distinguish the two species.

Dataset: Dataset for Dogs vs. Cats on Kaggle

Use-Case: This is a great project idea to learn how convolutional neural network (CNN), models are constructed using TensorFlow, Keras library, and Python.

4. Click My Selfie System

Gen Z is now obsessed with taking selfies! Because they are part of the generation that saw smartphones everywhere from birth, they’re learning faster. They are also not afraid to share their knowledge what they shared with their friends on social media. We came up with a great computer vision project idea to help our Gen Z, making an Automated selfie system that takes pictures of people smiling when they look at the camera.

Solution Approach: You can train the model to distinguish between smiling and non-smiling faces using a convolution neural network model such as VGG-16. After you’ve achieved an acceptable accuracy, test the model using your image. You can then use the OpenCV library and overlay the model on each frame. of the live camera and then trigger the capture if the smiling face is detected. Before you train the model, make sure that you perform face detection.

Dataset: Kaggle Smile-detection Dataset

Use-Case: Gen Z can use it to click their selfies. Many digital marketing teams can also benefit from campaigns that offer free samples for users who share their reviews on social media.

5. Text Recognition System

It can be difficult to visit a country where the language spoken is not yours. However, that shouldn’t stop people from visiting these countries and exploring the culture they offer. Computer vision technology has made traveling to other countries much easier. Computer vision technology is used in text recognition systems. These systems can recognize any language and translate it into the user’s language.

Solution Approach: The primary task of this project is optical character recognition (OCR). You can use Tesseract from Google to do it, along with an object detection model such as YOLO v4. Download the pre-trained YOLO Weights to create your own custom object detection model. Next, you can use labeling for annotations of the images. Next, use annotated images to train the YOLO model. You can also use Pytessaract to extract text from test images and predict the text.

Dataset: Text–Image-OCR Dataset on Kaggle

Use-Case: Implementing this Project for Language Translation Applications.

6. Digit Recognizer using MNIST

The MNIST dataset has become a very popular data set in the Data Science community. This dataset contains images of handwritten numbers and was created from resampling NIST’s original dataset. MNIST has approximately 70,000 black-and-white images with a size of 28 x 28 pixels. This dataset can be used to build a digit recognition system.

Solution Approach: This project’s first step will be to properly analyze the MNIST data. This will enable us to determine how the data should be processed before we can apply any algorithm. After the preprocessing and analysis are complete, you can create a CNN model to classify digits in Python. Once you are satisfied with the accuracy of your model, test it using testing images. To visualize the model’s performance, you can use a confusion matrix.

Dataset: MNIST handwritten digital database by Yann, LeCun, Corinna Cortes, and Chris Burges

Use Case:  This project is scaleable to create an application that can read handwritten text in various languages and convert it into digital information. Then, one can use language translation techniques to convert the text to their preferred language.

7. Image Colorization

Observe the old grayscale images so many of us have a hard ti to imagine the colors that the moment captured would contain. Computer vision technology is the perfect tool to ease our pain. It can be used to create a smart image colorization system.

Solution Approach: To implement this project idea, use the VGG-16 Model. After setting the parameters of the model, you can use ImageDataGenerator to rescale the images. Next, convert the RBG file to LAB. Next, create a sequential model using Keras for Autoencoders. Finally, test its performance with test images.

Dataset: Landscape Pictures at Kaggle

Use Case: This project is used to color historical images in order to get more information.

Also read: Top 12 Computer Science Project Ideas for Beginners

8. Social Distancing Tracker

One of the best ways to prevent the coronavirus is social distancing. This is when two people are kept at a distance of 2 meters. Social distancing rules are necessary if the virus is to be prevented from becoming a serious epidemic. Computer vision technology is a great tool as it can be used to estimate the distance between two people in a given frame.

Solution Approach: This project’s first step will be to train an object detection model such as Faster RCNN to identify people within a frame. After that, you’ll need to set the scale of pixels and then use that scale to convert pixel distance into the actual distance. A warning message will appear on the screen if that distance falls below 2 meters.

Dataset: Social Distancing Dataset

Use Case: This project is available at public places such as airports, bus stops, and markets to ensure social distancing.

9. Parking Management System

Many of us don’t like waiting in long lines to get a parking spot. The long lines for parking spaces will soon disappear, thanks to computer vision technology. This is because Artificial Intelligence technology can be harnessed to create an automated parking system that allows one’s car to be parked automatically.

Solution Approach: This project will include several mini projects such as number plate recognition, vehicle identification, and path identification. It also includes an auto-debiting system. You can train an object detection model to recognize vehicle license plates. Then, you can use computer vision to find the vehicle’s path using identification. Next, scan the records

Dataset: It is recommended that you spend some time creating your own dataset, particularly for this project. You can use Stanford Cars or Car License plate Detection as trial methods. Both are available on Kaggle.

Use Case: This project is suitable for use in shopping malls and metro stations. To speed up the process of parking.

10. Automated Attendance Systems

It can sometimes be difficult to keep physical records of students and employees at an institution due to the amount of space required. Software-based attendance systems, thanks to developments in IT, are now easily accessible. This has made it possible for the information to be stored digitally which is more efficient and convenient than registers. AI experts are working to automate attendance systems by using computer vision. This system can capture a person’s face and scan all the records stored to identify them. It will automatically mark the person present if the faces match one of the previously stored records.

Solution Approach: First, a CNN model will need to be trained to identify people who must be recorded. Then, you can test the system’s performance by sending an image of one person and then performing face detection. Next, identify the person using the CNN-trained model. After a person is identified, you can update the record by marking him as present in the database.

Dataset: It is a good idea to create your own dataset for this project. You can also use the CelebA Dataset.

Use Case: This project can be used by many companies to automate attendance systems.

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