Will computer vision take on human jobs in the coming years with the help of high technology?

There is a global controversy over whether computer vision is taking up human jobs, which can increase the unemployment rate. The integration of the latest technologies and artificial intelligence algorithms into an existing computer system has brought advanced computer vision. Artificial intelligence algorithms convert multiple real-time data into suitable business data without human intervention. However, artificial intelligence algorithms and computers require human assistance to perform many tasks efficiently. Have you ever wondered why the jobs of people in the field of artificial intelligence have increased in recent years? Human skills are needed to drive software development and innovate new technologies to increase productivity. Reputable companies and beginners offer job opportunities such as computer vision engineer, computer vision researcher, in-depth learning expert, software developer, data scientist, software engineer, senior researcher, data analytics director, computer vision research engineer and many others with a lucrative salary package. Nonetheless, we can argue that computer vision does not take on human labor, but effectively facilitates the workload to achieve a higher return on investment.

How does computer vision facilitate the workload of a person at work?

Computers analyze a number of raw data from digital images and videos to provide appropriate decisions by understanding the environment in the form of a new artificial intelligence known as computer vision. Data is more readily available and cheaper due to digital change and globalization. Computer vision has been successful in recent years because the recognition accuracy of image models is higher than that of humans. In-depth learning neural networks enable the iterative learning process of computers to acquire, process, and analyze image models more efficiently and effectively than the human visual cognitive system. The convolutional neural network (CNN) is used in computer vision technology to properly identify an image. These neural networks scan the available image pixel by pixel to identify patterns and memorize the ideal output for various properties such as outline and color. People are needed to develop intelligent machines to perform automated tasks using visual cognition.

A computer vision system can be used to classify objects, identify an object, and track an object. Analyzing thousands of images and detecting faults or problems with hi-tech cameras, data, and artificial intelligence algorithms takes much less time than with the naked eye. Investigate the potential of computer vision in several industries to improve productivity.

  • Auto industry: Computer vision using ADAS, RADAR, and LIDAR technologies provides a visual representation, good visibility, and a 3D representation of the environment. The recognition of car gestures also monitors drivers ’facial and hand movements with an audible signal.
  • Resale: Computer vision helps ensure security through CCTV, leak detection, theft management, video analytics, improving the shopping experience, optimizing operations, alerting shelf productivity, and improving customer engagement
  • Manufacturing: Computer vision helps factory workers with proactive maintenance, fault identification and risk elimination, and product quality control for minimal waste
  • Health care: Computer vision can accurately detect an unusual pattern in reports and x-rays, early-stage tumors, arteriosclerosis, and many other activities of doctors and nurses at the right time
  • Agricultural: Computer vision detects pests and plant diseases, information on high quality crops, recognizes faces to identify the animal and analyzes grain quality for farmers effectively

Therefore, we can all confirm that computer vision will not take human jobs, but it is likely that it will create more human job opportunities in the coming years. The goal of computer vision is to work with people to increase the workload with more appropriate results without errors or mistakes. We must remember that humans are the main creator behind all these achievements in computer vision.

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