Computer Vision in Artificial Intelligence - smartData TOP

Computer Vision in Artificial Intelligence

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.” From recognizing faces to processing the live action of a football game, computer vision rivals and surpasses human visual abilities in many areas.

Computer vision is used across industries to enhance the consumer experience, reduce costs, and increase security. Some areas particular to industries where CV (Computer vision) is being used are mentioned below:


Retailers can use computer vision to enhance the shopping experience, increase loss prevention and detect out-of-stock shelves. Computer vision is already helping customer checkout more quickly – aiding using self-checkout machines or combining with machine learning to alleviate the checkout process completely.


In manufacturing, businesses use computer vision to identify product defects in real-time. As the products are coming off the production line, a computer processes images or videos, and flags dozens of different types of defects — even on the smallest of products.


Public Sector agencies use computer vision to better understand the physical condition of assets under their control, including equipment and infrastructure. Computer vision can help agencies perform predictive maintenance by analyzing equipment and infrastructure images to make better decisions on which of these require maintenance. In addition, Public Sector agencies use computer vision to help monitor compliance with policies and regulations. For example, computer vision can be used to detect contraband in cargo, flag potential safety violations in buildings, review labels for adherence to guidelines, and ensure compliance with conservation regulations. Finally, as drones become used more for defense and homeland security needs, the use of analytics to identify and analyze critical elements from the visual feed will rise to the forefront of computer vision use cases in the public sector.


In the medical field, computer vision systems thoroughly examine imagery from MRIs, CAT scans, and X-rays to detect abnormalities as accurately as human doctors. Medical professionals also use neural networks on three-dimensional images like ultrasounds to detect visual differences in heartbeats and more.

Defence and Security

In high-security environments like banking and casinos, businesses use computer vision for more accurate identification of customers when large amounts of money are being exchanged. It’s impossible for security guards to analyze hundreds of video feeds at once, but a computer vision algorithm can.


In the insurance industry, companies use computer vision to conduct more consistent and accurate vehicle damage assessments. The advancement is reducing fraud and streamlining the claims process.

We at smartData have developed applications with computer vision for various industries. A couple of work references for the same are mentioned below:

  • Developed a surveillance system where we take live feeds from CCTV cameras to identify moving objects including walking people. Using facial recognition algorithm we identify the person from our database and display the information to various stakeholders over a UI interface. The application incorporates Computer Vision techniques and uses OpenCV to present the frames in the backend.

  • The application identifies wound size, shape, area, and volume using artificial intelligence (AI) to measure wound circumference, type, and progress. It is a platform that allows multiple care facilities to manage wound healing progress rates and wound treatment supplies. I have used the Image Segmentation technique to develop a solution in wound management where we measure the wound (length, width, tissue analysis), determine dead tissues through the Computer Vision technique. Have also used OpenCV for image recognition. The algorithm used has been trained using the wound images dataset. When the user inputs the wound image, analysis is being done and the wound is classified and various reports are being generated.

  • Developed a model which is used to process different clinical records using NLTK, NLP to extract textual data from them. In this, I have used tesseract as OCR to convert pdf, scanned images, and other docs to text and then process it with NLP models to extract the required information.

Contact us to know more about Computer Vision-based requirements.

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