COVID–19 has had a major influence on nearly every sector. Uncertainties, hardships, and opportunities now characterize the emerging new world. 

As a result of the unfortunate outbreak, remote work is becoming the norm. There are fewer resources available to work on-site due to the limits imposed on the work environment.

Almost every industry has begun to automate its procedures and systems. Automation has become a must to improve efficiency, reliability, and adaptability in today's environment.

This is where technologies such as Artificial Intelligence, Machine Learning, and Computer Vision may be useful. Let's take a look at the advancements that Computer Vision has already achieved, as well as its numerous uses across various sectors and advantages.

What is Computer Vision?

Computer vision (CV) is a subgenre of artificial intelligence (AI) that focuses on the development and use of digital systems for the processing, analysis, and interpretation of visual input. Convolutional neural networks (CNNs) are used in computer vision to interpret visual input at the pixel level, while deep learning recurrent neural networks (RNNs) are used to comprehend how one pixel connects to another.

The technology tries to replicate human vision to speed up the completion of repetitive or difficult visual activities. From an engineering perspective, It seeks to grasp and automate actions that the human visual system is capable of.

Computer Vision applications have grown since the introduction of the first commercial Computer Vision software in the 1970s, from allowing reading devices for the blind to altering whole industries. Today, some Computer Vision-based systems have attained 99 percent accuracy and can even outperform humans (for instance, in diagnostic radiology).

What is the potential of Computer Vision? — The most important Computer Vision approaches

Computer Vision applications nowadays are capable of incredible feats. This is because they can do any or all of the following fundamental tasks for various industries and verticals:

  • Object classification that gives predetermined classes to objects in a picture The system uses classification model to answer a basic query about whether an object belongs to various categories, e.g. A class of berries, exotic tourist destination, or breed of dogs.
  • Object localization leverages a bounding box to locate an item in an image.
  • Object detection that applies both of the above to a large number of objects in an image, assigning labels and locating them by drawing bounding boxes around them.
  • Semantic segmentation by understanding every pixel of an image and associating it with a class label (a fruit, a person, an animal, etc.) by using object masks to show items of the same class as a single entity
  • Instance segmentation accomplishes semantic segmentation and distinguishes between distinct instances of the same class. For example, If there are three parked cars in a street view image, they don't simply get a one-color mask but are labeled with three different colors, indicating their borders.

Computer Vision: All industry-wide use cases examples

Computer Vision applications have grown at a breakneck speed over the previous decade, reaching a fever pitch with the start of the COVID-19 epidemic. From retail software and healthcare platforms to sophisticated manufacturing and government systems, companies are investing substantially in AI-driven solutions.

Check how organizations are employing sophisticated Computer Vision solutions to increase their outcomes, here is a collection of successful Computer Vision use cases in the  key sectors:

Industry: Construction and Infrastructure

Client: LiDAR and Drones Manufacturing firm with 200+ employees in Texas.

Use Case: Point Cloud Segmentation using LiDAR

Using LiDARs, the client helps gather and survey data with sensors mounted on drones, aircraft, vehicles, and UAVs. The primary customer of the client includes infrastructure, oil and gas, and construction companies. Detecting if vegetation is growing near electric poles, which might impede the electric wires and cause outages, is one of the use cases tackled. This saves the infrastructure firm a lot of money on labor by reducing the number of people who have to go on-site to audit power wires that can be hundreds of kilometers long.

Technologies Applied: TensorRT, CUDA-X, PyTorch, Python, C/ C++, Kafka, Django, Flask.

Use cases solved:

Point Cloud Segmentation: Gather point clouds using LIDAR and classify them into multiple classes like buildings, cars, vegetation, electric poles, and 17 other classes.

Industry: eCommerce


  • Marketing Firm in Münich, Germany with 20 – 50 employees.
  • Digital Transformation firm in Sydney, Australia with 20 – 50 employees.

Use Case: Image-Based Recommender System

This solution helps e-commerce companies recommend visually and contextually similar items to help boost revenue. Example user flow is: let’s say you like a t-shirt while surfing on Instagram. You take a screenshot and upload this picture to this service. The web app recommends visually similar t-shirts from the prefilled database that is connected to the database of fashion brands. On purchase from the web app, it gets a 5% cut from the fashion brand.

Technology Applied: Python, C/C++, Django, OpenCV, Scikit-learn

Use cases solved: 

  • Object Detection: Detection of 2000 different types of common objects.
  • Image Segmentation: To Detect and mask background clutter for accurate object detection.
  • Image Matching: Compute image similarity and perform image clustering to perform precise image retrieval. 

Industry: Healthcare

Client: Proof of Concept for a Major hospital in Pune, India.

Use Case: Thermal Screening

As the Covid-19 restrictions are relaxing, significant offices, schools, universities, and transport are opening up. This solution uses a thermal camera to detect elevated temperatures on the people passing through. The solution requires minimal intervention and sends real-time alerts to screen for possible illness in pedestrians, travelers, or working professionals.

Technology Applied: TensorRT, CUDA-X, PyTorch, Python, C/ C++, Kafka, Django, Flask.

Use cases solved: 

  • Person Detection: Detect and count people passing through the entry gate or corridor.
  • Keypoint Detection: Detects unique points on the person’s face for accurately determining elevated temperatures

Industry: Retail and Manufacturing

Client: Computer Vision giant in the Bay Area. Deployed in production at over 2 locations in the US.

Use Case: Inventory management

Identify empty shelves for easier stocking and products that sell out faster.

Technology Applied: TensorRT, CUDA-X, PyTorch, Python, C/ C++, Kafka, Django, Flask, Statistical Machine Learning.

Use cases solved: 

  • Shelf Detection: To Detect the inventory shelf boundaries using Image Processing.
  • Dense Object Detection: Detect highly dense objects like bottles, packets, and items on a shelf

Industry: Security and Surveillance

Client: Proof of Concept for a paint manufacturing company in Texas.

Use Case: Forklift Signaling

Manufacturing companies have several forklifts operating simultaneously in factories. This poses a threat to the workers who are working in the same area as the forklifts. This solution serves as a signaling mechanism to avoid collision between forklifts and provide safety to the workers.  

Technology Applied: TensorRT, CUDA-X, PyTorch, Python, C/ C++, Kafka, Django, Flask.

Use cases solved: 

  • Forklift Detection: Detect and track the forklift as it passes by in the aisle
  • Path and Velocity prediction: Predict the velocity and path trajectory of the forklift
  • Person Detection: To Detect nearby persons to predict possible collisions.

Industry: Smart City

Client: Deployed in production at 10+ locations in Pune, India.

Use Case: Automatic Number Plate Recognition

Automatic Number Plate Recognition serves several use cases from parking management access control to surveillance purposes. Adagrad developed “Gate-Guard,” a proprietary solution for automated access control. Primary customers include Residential Complexes, Offices, and Smart Cities.  

Technology Applied: TensorRT, CUDA-X, PyTorch, Python, C/ C++, Kafka, Django, Flask.

Use cases solved: 

  • Access Control at the gate: Authorize pre-registered vehicles, including bikes, cars, transport vehicles, and more
  • Barrier Operation: Actuate boom barriers using a custom-developed Android application on a tablet
  • Analytics Dashboards: Allow the stakeholders like security professionals administrators to access the records of visitors and authorized members.
  • Occupancy: Enable the security staff at the gate to allow visitors to park their vehicles inside the premises and disallow if the parking is full.

The reasons why various industry verticals are opting for Computer Vision as a solution and the fundamental causes fueling the rise of Computer Vision applications:

  • The expansion of visual data is fueled by mobile technology's proliferation, which allows us to generate billions of photographs every day.
  • Computer processing power is becoming increasingly affordable to handle this massive volume of visual information.
  • State-of-the-art and innovative Hardware
  • Algorithms for deep learning have progressed.