By Bernadette Wilson, on Apr 23, 2020

The Growing Need for Image Labeling & Annotation

Industry analysts are predicting a surge in the data preparation market. Market research firm Cognilytica, for example, predicts the global data preparation market will grow from $500 million in 2018 to $1.2 billion by 2023. Among the most common data preparation workloads expected to increase in the next few years is image labeling and annotation to create training and testing data sets for object or image recognition models.

What’s Driving Image Labeling Demand?

The primary reason for dramatic growth anticipated in the image labeling market is the current boom in artificial intelligence (AI) adoption. Accenture calls AI the savior for businesses that aren’t able to sustain growth with their current labor forces and capital — AI technology has the potential to nearly double growth rates for businesses that are now only sustaining their current level of production. In fact, Accenture studied U.S. businesses before and after deploying AI-based systems and found that they experienced growth, on average, from 2.6 percent to 4.6 percent. AI systems’ capabilities that enable that growth include intelligent automation, labor force capability augmentation, and opening doors to innovation.

You can find a multitude of applications for AI and computer vision across a wide range of industries. It can enable effective track-and-trace systems that save time locating assets or products. Inspection systems using AI can heighten quality control. AI is also integral to predictive maintenance systems that collect data about mission-critical operations and alert teams to potential problems before they lead to costly downtime.

Supervised Learning for Computer Vision Systems Requires Image Labeling

To develop an AI machine vision system that produces desired outcomes, first, you need to train the model to recognize images and objects. Even though the camera of a machine vision system can “see,” it can’t interpret what it is seeing and trigger action without training.

Machine vision systems learn to recognize images from a training data set of labeled images. A data labeler collects relevant images, perhaps photos or video of products on a conveyor. The data labeler, using a data labeling tool, draws bounding boxes around specific images and annotates them in a way that a computer can understand. In this case, the data labeler can draw bounding boxes around specific parts of correctly assembled products. The training data set teaches the AI machine vision model to recognize products that are properly assembled and those that are not, enhancing quality control. The system levering AI technology may also have the capability to alert an operator when a substandard product passes by or even trigger actions to halt production to minimize waste from defective products.  

Why Businesses Are Outsourcing Image Labeling

It’s crucial to train a machine vision system with representative images, labeled accurately and consistently. Without a solid training data set, the results the AI system delivers in the real world won’t be accurate or reliable. To ensure acceptable outcomes, data labelers must annotate an adequate number of images for the model to work. Typically, that doesn’t mean only labeling dozens or even hundreds of images to create a data set. For a complex system, it could take more than a million images to ensure desired results — and this is often more work than a business can handle in house.

The workload associated with data annotation is one justification for turning to a third party for image labeling services. There are, however, other compelling reasons for outsourcing data labeling:

  • Quality: Experienced data labelers are more likely to produce a data set that delivers accurate, real-world results.
  • Speed: A data labeling service has the tools and resources to complete a project more quickly than a single employee trying to label images as well as taking care of their other job responsibilities. Faster data preparation means faster machine learning system deployment.
  • No bias: Using a third party for data labeling services ensures that the people labeling the data aren’t influenced by a preconceived notion of how the machine vision system is supposed to work or by cultural biases.

ROI from using a data labeling service is definitely there. Businesses get a high-quality data set in less time — helping them to get their AI machine vision project running sooner — at a lower cost than if they had kept the work in house.

What’s in Store for the Future?

Adoption of intelligent automation and innovation powered by AI is destined to become a standard capability for many organizations that want to remain competitive. Data labeling services are a key component of implementing machine vision systems that accurately and reliably work. Are you ready to help make it happen?

Opinions expressed by Daivergent contributors are their own.