Edge Computing for Computer Vision: 3 Reasons Why Cloud Is Not Enough (And Why We Still Need It)

In this article you will learn why edge computing is useful and what edge computing does for computer vision applications. Particularly, we will cover:

  1. What edge computing is
  2. Benefits of edge computing for computer vision
  3. How edge computing works with the cloud

Edge computing is essential to computer vision processes for bandwidth, speed, and security purposes. Edge computing tends to be a cheaper, more secure option for data processing as it relates to computer vision. Although the majority of data processing can happen on the edge, results are often sent to the cloud for further analysis. Business analytics and aggregation can occur when edge computing data is sent back to the cloud.

What Is Edge Computing?

Edge computing describes computing that occurs near the source of data, rather than depending on cloud networks far from data access points to do all necessary computing. End devices refer to phones, laptops, TV’s, and includes any device that shows the frontend of data processing. Meanwhile, computer vision is used by corporate networks to fulfill any tasks that would ordinarily require the human eye. Edge computing is essential to computer vision operations because it improves response time, saves bandwidth, and is more secure than most cloud computing platforms.

 

Edge computing on a larger scale

 

Why Is Edge Computing Useful?

  1. Real-time and low latency. Since edge computing allows data to be closer to the endpoints of networks, data can be accessed in real-time with little to no buffer. This allows computer vision operations in remote and urban places to have faster response times. Locations without efficient network infrastructure can also be aided quickly. A hospital in a semi-rural location using robotic surgery would need access to its computer vision data in real-time. Having the 6-9 second buffer that occasionally comes with cloud connection would be detrimental to time-sensitive use cases such as this one.
  2. Bandwidth. Edge computing also has no dependency on a central data center or network bandwidth. Computer vision being processed through edge computing allows for reduced data transmission cost, since you wouldn’t need to access the cloud nearly as much.
  3. Data privacy. Computer vision involves video data. Video data is sensitive because it involves people’s personal identification information and other secure images. Sending video data to the cloud and storing it there increases data security risks. Edge computing processes the video material, and no video data needs to be sent to the cloud or stored in the cloud. The risk of exposure to security threats is reduced since edge computing distributes risk of exposure across multiple devices. Edge computing can perform all processing disconnected from a central server. Therefore, its private architecture allows it to be more secure. Autonomous vehicles utilize computer vision data on a regular basis. Having on-vehicle edge computing rather than cloud-stored computing reduces the chances of an automated vehicle getting hijacked. Additionally, data isn’t shared with multiple developers. This element of privacy makes it much more difficult for hackers to attack edge computed systems.

What Are the Disadvantages of Edge Computing for AI Vision?

Involving all edge endpoints can create complex device architectures. This makes device management difficult and edge computing complex to implement. Unlike cloud architecture, which is centralized around one common cloud, edge computing is distributed. This means there are more network combinations with several computing nodes. It can be inferred that such an infrastructure would require higher maintenance cost for large companies than one centralized around the cloud. However, device management systems can keep the costs low and the architecture simple to maintain. This enables corporate users to utilize the benefits of both, cloud and edge, and perform each calculation task where it is most cost efficient.

What’s Next?

Due to the security and speed of edge computing regarding computer vision, it is an ever-growing industry and one of the major trends in IT. More and more companies are trusting edge computing in combination with cloud services with their data because of its streamlined approach. If you want to learn more about where computer vision is applied, you can read about the 28 Most Popular Computer Vision Applications in 2020.

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