Have you ever thought about how your smartphone unlocks the screen with your face within seconds? These are hustle and bustle decisions that make technologies smart, but there is a secret sauce behind it: computer vision. It allows machines to think and understand like humans. If you are a student interested in artificial intelligence, then learning computer vision can open many exciting opportunities.
When you work on practical projects, you do not just learn theory; in fact, you learn blows your mind. You actually learn to see how machines interpret visual data. You learn many tools, such as Python, OpenCV, and deep learning frameworks. Resultantly, it makes complexity easier for you to experiment and create solutions.
In this article, you can make your portfolio stronger and gain practical experience. You can easily explore the computer vision projects list, which helps you from beginner to advanced levels.
Beginner-level Computer Vision Projects:
Most computer vision projects are tough enough to start due to a complex problem. But when you start your journey, you need to search for a project that gives you plenty of experience in your hands. At this stage, most projects are related to classification or detection techniques. So here is a list, you can go through to find yours:
1. Face Mask Detecting CV Project:
The computer vision project of detecting face masks is very important in developing your basics. Doing such a project holds great importance in its core because the model is trained enough to identify whether the person is wearing the mask or not. These types of projects are very important, especially when the world has faced a very serious issue of epidemic disease like COVID-19. This is really a very lucrative opportunity to do such projects that hold two domains in them, like object detection and facial analysis.
You can easily load the datasets into your database and build a CNN model using TensorFlow or PyTorch. Then, train the model according to the datasets. At last, you can implement detection using OpenCV. If you do a lot of practice with a real detection system, it would be a plus point to demonstrate your skills in performance optimization.
2. Traffic Signs Detecting CV Project:
This project has a huge importance in dealing with image detection when you are dealing with autonomous driving and image classification categories. Your model is trained enough to make a category of objects and easily dictate the board information about what they are telling.
This declares your image classification skills and how much you are into training your model. You load and preprocess the datasets into the model. According to the datasets, you design a CNN architecture and then train the model. At last, you create a simple UI for testing the new images.
3. Plant Health Detecting CV Projects:
This project is very crucial in dealing especially with the agriculture department. Farmers are very confused about telling the health of a plant and what deficiency makes a plant dull. When you do such a project, it not only enhances your skills but also gives you a great knowledge of the crop industry.
You easily take plant images of their leaves, stems, and bodies. Your model is trained enough to tell whether the plant is nutrient-deficient or the weather is disturbing its health. You easily load the datasets and use transfer learning to pre-train the model, like ResNet. Then tune the model on the plant health requisite datasets. At last, you build a web application on the plant health model.
4. Clothing Classifier CV Project:
This project empowers you enough to make a classification of clothes categories. It deals with the type of clothes, whether they are soft or hard, and what type they belong to according to the season requirements. This is really holding an edge in the fashion industry, where, if a person doesn’t know the type of clothing, they can easily tell which seasonal clothes they love to buy using this model.
Intermediate-Level Computer Vision Projects:
When you are trained enough to fundamental skills like classification, detection, and build up simple user-interaction models. It is time to tackle some more complex models. Here is a list of projects that upgrade you to the next level and make your portfolio much stronger.
5. Object Tracking Short or Long Video CV project :
Detecting objects is really a very hectic task, and training the model is a big task. You must build a system for tracking multiple objects in long or short video clips. And after the process of detection, your model is trained enough to make the categories of the object as either an animal or a human. Developing such a model makes you a demanding candidate in the sports and autonomous vehicle industry.
You implement object detection by using YOLO or faster R-CNN. Then you apply a tracking algorithm like SORT or DeepSORT. It also helps you optimize real-time performance.
6. Gesture Recognition CV Project:
In this project, you collect multiple body movement datasets into the model. Train the datasets and apply the desired settings to achieve the ultimate goal. There are many open sources available, like ASL (American Sign Language) datasets and the Hand-Foot Gestures datasets. When these types of datasets are introduced to your model, they make the system alive to detect the body gestures.
To build this project, you must collect your sample datasets and analyze them according to the feeding datasets. For better results, you continuously hyper-tuned the variables. These projects are very demanding in gaming, VR, and accessible technology, where the first user interface is unlocking your screen from your movements.
7. Automatic Image Captioning CV Projects:
Automatic image captioning projects are one of the best projects in which you combine CV and NLP for visionary and translational purposes. Your main goal is to make a visionary model with auditory ability that combines with it. This project adds more ability to your experience level. You work independently with complex and multi-model problems. Find a perfect solution.
After working with this type of project, you gain a practical understanding of feature extraction and transformer-like architectures. You use a pre-trained CNN for image feature extractions. Then, incorporating this with an LSTM for a caption generator. Train the model end-to-end with the COCO datasets. At last, you create a web interface for uploading and captioning new images.
8. Question Answer Dashboard (QAD) CV Project:
This is another project that incorporates the CV and NLP together to make a console or dashboard of questions and automatically generates the answers. When an image is uploaded into the model, it automatically suggests the answers with suitably generated questions. You have the perfect skill to deal with multi-model data (images plus text ) and to design and train the complex networking architectures.
You implement the image feature extraction by using a pre-trained CNN model. You apply your skills here to design a series of automated question pipelines for model efficiency. Then, you apply a fusion of networks combining image and text features. At last, you feed the question and answering dashboard datasets into the model for better results.
Advanced-Level Computer Vision Project:
Once you grow from beginner level and solve more projects, you can handle the toughest projects easily. Every challenging task gives you a light to come out of the dark world. From your continuous practice, you do a lot. Here is a list of projects that make your portfolio presentable:
9. Image Deblurring CV Projects:
In this project, you remove the blur from the image easily. Sometimes, you see high-precision cameras, but the result of the image is so bad that you cannot see the desired objects. So, you need to learn how to improve the image quality by removing blur and noise. These types of projects are used in photography, medical imaging, and satellite imagery.
You can easily implement data preparation and processing of datasets. From this, you can develop a multi-scale CNN or GAN model. You introduce evaluation metrics such as peak signal-to-noise ratio (PSNR). You optimize the model for checking its speed and accuracy.
10. Suspicious Element Detection CV Project:
In this project, you add multiple images to the model. Your model is then trained sufficiently to detect unwanted or suspicious elements in the images. It incorporates CV solutions and learn from every image what matters the most. The project deals with and analyzes every aspect of unwanted things and saves all the information in its memory
These types of projects are related to unsupervised problems and involve working with specialized industrial datasets. These projects are a highly valuable addition to your portfolio.
You can easily implement an autoencoder architecture for sample reconstructions. Always try to train the model on normal samples only.
11. Video Summarization CV Projects:
These types of projects are key and featural thing to your skill in video editing. Sometimes, videos are too long to be handled and consume a lot of your time to proceed. You can easily escape that situation when you read that video in a script or summary form.
In this project, you mix the concept of CV with NLP. This thing empowers you more to make a very progressive model that holds a huge amount of datasets. Handling this type of dataset really requires a lot of your skills. So, you can easily handle and cover every detail of the video in a summary form, like short detection, feature extraction, image processing, and video analytics.
12. Face De-Aging/Aging CV Projects:
In this project, you have a lot amount of datasets of human face images with their ages. The main purpose of this project is to build a progressive network that can make a category of age and de-age groups. So , when a sample is uploaded into the model. It easily compares the image and relates whether its selected object is aged or de-aged.
These projects are high in demand, especially for entertainment, forensics, and privacy protection. The projects involved generative modeling and building complex GAN architectures. You can easily preprocess and clean the datasets. You learn to implement a cyclic-consistent GAN architecture. Train the model on aged or de-age datasets and easily develop a web application
FAQ’s:
What are the most challenging things you face in your computer vision journey?
The most challenging thing a person faces in the computer vision journey is the limited time availability of training the datasets, hardware limitations, and data over-fitting.
How can you overcome the challenges of the computer vision journey?
You can overcome these challenges :
- By implementing data augmentation techniques to increase the size of the training datasets.
- By using hardware accelerators to improve model performance and the rate of actions.
- By applying regularization techniques to reduce data overfitting.
How can you estimate the performance of a computer vision model?
You can easily estimate the performance of a computer vision model by analyzing every task and the project’s purpose. If your aim is a classification task, then you should apply classification metrics to study the categories. If your aim is object detection, you should apply mean average precision, and the intersection over union is used.
Conclusion:
Computer vision is a tremendous field of artificial intelligence. But when you do practical projects, ultimately you learn faster. By working on different computer vision project ideas, you handle all types of datasets easily and tune them easily. Your skills improve a lot when you do practice by using tools like Python, OpenCV, TensorFlow, and PyTorch. Ultimately, it strengthens your portfolio. By doing multiple practices and experimentation, you can easily develop technical solutions in every sector.
Reference Link:
https://onlinelibrary.wiley.com/doi/full/10.1155/2018/7068349
