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3 min read ● Silk Team
Although there are numerous applications of computer vision that provide a wide variety of benefits to users and society, several challenges exist to achieving widespread adoption.
First, the development of quality computer vision technology has historically been expensive, making the cost barrier for developing a viable application prohibitively high. Although many of the costs associated with building and maintaining computer vision technology have decreased dramatically over the years, they are still significantly higher than those associated with other types of software technologies.
Second, computer vision technology is often difficult to develop and deploy. Building and deploying computer vision solutions requires substantial technical expertise, including skills in image processing, machine learning, artificial intelligence, programming languages such as C++ and Java, and operating system design. This expertise can be difficult to find, especially for companies that do not specialize in the development of computer vision technology.
Third, computer vision technology is sensitive to environmental factors such as lighting and camera angles. When computer vision systems are used in environments where lighting and camera angles cannot be controlled, the accuracy of the results may be compromised. For example, if a video surveillance system is designed to detect objects based on color, but lighting conditions change throughout the day, object detection accuracy may decline.
Fourth, while computer vision technology has the potential to reduce labor costs in certain industries, its impact is often limited. Many workers perform tasks that require manual dexterity and hands-on interaction. While computer vision may allow some workers to focus on more strategic aspects of their roles, it is unlikely to fully replace most of the workforce in these industries.
Finally, there are concerns about privacy and the misuse of computer vision technology. Computer vision systems can potentially be used to monitor employees without their knowledge or consent. Additionally, they can gather information about people’s behavior and activities in ways that may violate privacy rights.
As a result, the challenges associated with implementing computer vision technology will likely continue to limit its widespread adoption. However, once the obstacles related to development, deployment, cost, and governance are addressed, the benefits of computer vision will become more apparent, and adoption may grow significantly.
Existing and Emerging Applications of Computer Vision
A large number of computer vision applications exist today, each providing a unique set of benefits to users and society. These include:
- Object recognition
- Facial recognition
- Gesture recognition
- Image-based search
- Video compression
- Medical imaging
- Quality inspection
- Robotics
- Remote sensing
- Security systems
- Video surveillance systems
In addition to these established applications, computer vision technology is being developed and applied to additional areas such as:
- Document analysis
- Autonomous vehicles
- Video conferencing
- Virtual reality
Summary of Key Adoption Challenges
While the potential benefits of computer vision technology are vast, several challenges continue to limit widespread adoption:
- High development and maintenance costs
- Technical complexity in development and deployment
- Sensitivity to environmental conditions
- Limited ability to fully replace human labor
- Privacy and ethical concerns
Addressing these challenges through improved tooling, standardization, governance frameworks, and cost reduction will be essential for unlocking the full societal and economic potential of computer vision technology.