Webinar Recap: Exploring Emerging Use Cases for Computer Vision
By Amy Townsend
This webinar covered the following topics:
- Computer vision’s power to automate and transform business processes.
- Examples of emerging use cases in computer vision–ranging from insurance to manufacturing.
- Managing computer vision systems in the hybrid workplace.
This blog gives a summary of the information Adam presented in the webinar.
To watch the webinar on-demand, click here.
What is Computer Vision?
Computer vision refers to the use of algorithms to understand, analyze, and reconstruct visual scenes from image or video data. Often, computer vision will go beyond other modes of digital image processing to analyze the 3D structure of a scene by identifying edges and shapes.
Computer Vision Technologies
Some examples of computer vision technologies that are already proving to be transformative in the market today include:
- Personnel Tracking–such as in a security system
- Object Identification–such as recognizing a school bus in the previous slide
- Autonomous Vehicles–as in the case of drones
- Document Understanding–which can enable the analysis of legal forms
- Content Generation–for use cases such as human faces, fashion, and shopping websites
- Product Quality Check
- Security Surveillance–already being put to use by police forces (e.g. traffic)
- Facial Recognition
- Visual Search–discover video content with more control and precision
- Robotic Automation–a major emerging example of computer vision
Use Cases for Computer Vision
Some industries where computer vision is making a difference include:
Whether it is counting livestock and monitoring that they are in the right place, or looking out for visual signs of distress or sickness, visual monitoring is an important part of animal health. Likewise, crops must be monitored and have their visual characteristics analyzed to ensure good growth. This brings us to harvesting, which is another process that depends on the human eye to tell when and how to pick produce.
By employing visual identification, recognition, and analysis, computer vision can transform many of the visual processes that enable agriculture.
The primary benefits come in the form of quality and efficiency from more reliable monitoring of crops and animals. Computer vision can add consistency and redundancy to processes that improve their efficacy. This can transform the economics of the farm.
Challenges to implementing computer vision may come in the form of scaling difficulties, depending on the size and setup of farms, or hesitancy on the behalf of some buyers due to the high-stakes nature of automating processes that are essential to farm health. Finally, there may be regulatory barriers, especially when it comes to livestock, for the way farms can be managed and their ability to utilize cameras.
For assets such as homes or office buildings, insurance providers must maintain accurate and up-to-date assessments of property value or other important factors related to the insured asset. Claims themselves must be assessed on the basis of an accurate understanding of the asset’s value and quality.
Computer vision can help by leveraging its analytic powers up close—in the case of image analysis—and at a distance in the form of ongoing satellite photography.
Insurance carriers that make use of computer vision to transform their business model have experienced productivity gains by eliminating some of the human work required in processing claims, and they can use new technology to streamline the experience of submitting a claim.
Insurance providers will need to ensure the customer experience is accounted for and that customers are comfortable with the introduction of AI into the claim process.
Workplace safety is important in the high-stakes context of a manufacturing setting. The need to manage a large workforce around dangerous machinery can put a strain on management and limit factory scaling. The need for product quality assurance and precision instrumentation are two other examples of processes that can limit a manufacturer’s scalability.
By analytically modeling 3D objects and analyzing continuous camera imagery for various factors, computer vision can transform the way factories handle these core legacy processes.
For enterprises, the ability to handle these key processes with computer vision can save costs, enable efficiency, and promote workplace safety.
Factories that want to make use of computer vision may encounter challenges, depending on their scale, or face potential pushback from workers and managers who may find that different responsibilities are being automated.
Considerations to Make Before Implementing Computer Vision
Here are some questions you should consider before implementing computer vision into your enterprise:
1. What business processes could be transformed through computer vision?
2. What computer vision technology would be a cost-effective investment?
3. What are the effects on the people and culture of the workplace?
Organizations should look at their entire business model and identify any areas that depend on visual identification, recognition, or analysis. Enterprises should understand their visual processes as hotspots for innovating with computer vision.
After auditing the business process for potential sites of innovation, enterprises should assess available computer vision technologies and determine which specific capabilities fit the business process they hope to transform.
Lastly, enterprises should evaluate the offerings of providers for cost-effectiveness and take steps to pilot tests of computer vision technology.
For guidance on implementing computer vision into your business model, consider scheduling a complimentary inquiry with Adam.
Click here to watch this webinar on-demand.