10 plus Project Ideas for Computer Vision

August 17, 2022

The aggressive growth of large data during the last decade drives for innovative methods to  extract high semantic information from raw sensors such as videos, images & speech  sequences.  

Deep learning is a sub-head of Machine Learning and a mechanism of Artificial Intelligence. It copies the work of human brain-like feature extraction through neural networks which has  a statistical collection of techniques in the form of layers; manual interpretation of data is not  required here.  

What is Computer Vision? 

Computer vision is seeing objects and extracting information based on the visuals. The  algorithms of computer vision analyze certain criteria in images and videos then apply  exposition to predictive or decision making tasks. 

Pipeline of computer vision:-

The range of project ideas that deep learning enables computer vision is wide. Here we have compiled 10 Plus Project Ideas for Computer Vision-

1) Image Classification:- It involves the recognition of objects within an image or video.  Example, MNSIT data set, it contains a set of alphabets or numerals (0-9) in handwriting. Image classification is a prominent deep learning application of computer vision. We can test out multiple algorithms without facing technical issues. Image classification focuses on separating the pixels of a picture according to the classes they belong to. You’d have to build a convolutional neural network through these AI tools for completing this project. It helps in not only understanding image classification but helps to discover most relevant AI tools of the industry TensorFlow & Keras.


2) Object Detection:- It is a part of image classification but works on large frame  images which have lots of objects. Object Detection is a technology where things, human, building, cars can be detected as objects in images and videos. Fig 2. Classification, Object Detection and Segmentation Representation Eg:- A street scene, there are pedestrians, cars,  streetlights etc. that requires special attention. We can achieve this by drawing a bounding box around each object separately. 

This project can help in understanding the Convolutional Neural Networks (CNN) which are a class of neural networks that have achieved state of the art results on difficult object detection problems. A traditional feedforward network flattens the matrix of pixel values, resulting in the loss of the spatial structure of the input.

3) Object Segmentation:- It is almost similar to object detection but rather than using a bounding box, it detects the object from pixel. Eg, a group of people in a photograph. 

Types Object segmentation:

  • Semantic segmentation: In Semantic Segmentation a label is assigned to every pixel in the image. This is a major contrast to classification, where a single label is allotted entire picture. Semantic segmentation treats multiple objects of the same class as a single entity.


  • Instance segmentation: Instance segmentation treats multiple objects of the same class as distinct individual objects . So, instance segmentation is harder than semantic segmentation.

This Project helps segmenting the objects and finding granular details about the object by creating pixel wise masks for each object. 

4) Style Transfer:- This application changes the object’s style according to a desired  style. It learns the respective style of an image and applies it to a new image.  It aims to transfer the style of one image onto another image, known as the content image. This technique allows us to synthesize new images combining the content and style of different images. 

In NST (Neural Style Transfer) style as texture is imbibed by Gram Matrix Features which are interrelated characteristics extracted from the many layers of a pre-trained deep convolutional neural network trained on ImageNet dataset for the task of object classification.

This project helps in styling an image into an entirely different base component showing how the high frequency components have increased. Also, this high frequency component is basically an edge-detector.

5)  Image colorization: Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input. In image colorization, a color is assigned to each pixel of a target grayscale image, specifically popular historical photos.

Decoloring images and videos and recoloring them back enables in terms of advantages in grayscale images and video features. Without making benefit from colorization algorithms, It can take a few days to several months for colorizing a single gray-scale image manually depending on the complexity of the pictures making it irregular,  so it’s worth researching the colorization problem. 

In general, the problem is to achieve automatic conversion from gray-scale image to colorful image with GAN networks. The input of the networks is a gray-scale image which only contains the brightness information, and the output would be the colorful image with the same size.

 6)  Deep Dream: Deep Dream is a computer vision program created that uses a        convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream -like psychedelic appearance in the deliberately over-processed images. 

This Project helped scientists and engineers to see what a deep neural network is seeing when it is looking in a given image in the initial phases. Later the algorithm has become a new form of psychedelic and abstract art. It is highly useful to embrace the visioning of a simple image with textures that are hallucinatory. 

7) Detecting Barcode/ QR Scanner-  It is based on scanning and detecting qr codes and with inclusion of pose estimation for optimization in detection of qr code and displaying a pyramid over it virtually and storing the read data from qr code. 

This is a program that scans the QR codes and Barcodes from an image. For this program, we need packages like OpenCV, NumPy. Most of the python programmers are familiar with OpenCV and Numpy libraries. OpenCV is an open-source computer vision and machine learning library. It is a useful library for image processing. 

Computer vision has opened up a lot of possibilities with the use of QR Codes. It is clear that they play a significant role in real life through data encoding, high data storage capacity, and their ability to provide secured access to information. They are increasingly present in products, advertisements, retail industries as well as in authentication and identification systems. 

 8) Car’s Number Plate Reader- The project is about car number plate recognition. In this project, a program needs to be developed that can automatically read the number plate of the vehicles. It is an image processing technology used to identify vehicles by their plate. 

The idea of this project is that a camera will take an image of the front or rear of the vehicle. Then, the image processing software will analyze the images and the information will be extracted. The image processing analysis will be done by using MATLAB.

Several compounding factors make this project incredibly challenging, including finding a dataset you can use to train a custom Number Plate Reader!! Large, robus datasets that are used to train state-of-the-art models are closely guarded and rarely (if ever) released publicly. 


9) Face Mask Detection-  As we know that all governments around the world are struggling against COVID-19, which causes serious health crises. Therefore, the use of face masks can slow down the high spread of this virus. 

This project helps in detecting people with/ without the masks. This process undergoes two phases-

  • Train model using Convolution or any pretrained model which detects face masks in images .
  • Then, Detect faces in video or images and get predictions from our trained model. 

10) People counter-   A growing business needs to invest time in analyzing its customers' behavior. This Project can help them in tracking which days specific discounts should go live. Thus, trying to build a computer vision-based people counter. 

It detects whether the frame contains a human being or not and then increases the counter for each unique human being the system detects. This project is a step ahead of the facial recognition system and exploring libraries like NumPy, OpenCV is a must for this project. 

This project requires object tracking. So, while building a solution to this, make sure you use the object detection techniques for each video frame.

11) Virtual Invigilator- AI-powered Invigilator is a technological solution to host the examination without the need for a human invigilator. Artificial Intelligence is employed to carry out the invigilation activity without compromising the integrity of the exam. A prerequisite for a remotely invigilated exam is that students must enable monitoring on their devices by accepting sharing their screen, video and audio. 

Following are the benefits of this project-

  1. Analyzing the glance 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. Locating mobile devices if any.

12) Vehicle Counter- Vehicle counting in a congested traffic road where background subtraction gives lower performance. Vehicle detection, tracking, counting and speed prediction on videos with OpenCV. OpenCv based live vehicle counting system written in C. This approach is based on the most efficient action detection and tracking methods in computer vision. YOLO is used for object detection, whereas Kalman filter with Hungarian algorithms are used for tracking.

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Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

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Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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