Improving Food Preparation with Data Annotation

 Computer vision allows systems to extract valuable information from digital images. A wide range of industries, including some surprising ones, are using this powerful tool. The fast-food industry is not traditionally associated with cutting edge AI developments.

 However, leaders in the sector are beginning to see the potential value of computer vision for improving product quality and customer satisfaction. Recent changes in consumer behaviour created by the COVID-19 pandemic have increased the pressure on restaurants. As a result companies are turning to AI innovators in the hopes of streamlining kitchen operations and helping staff.

 This blog will first identify the problems facing the restaurant industry in a changing world. Secondly, we will explore the specific AI applications being developed for food preparation. And finally, we will show how outsourcing data annotation can lead to better AI training datasets for developers working in this area.

Dealing with increased demand

 COVID-19 has led to a dramatic increase in demand for fast-food.  As governments reduced or banned in-restaurant dining many customers instead opted for drive-thru or delivery. As a consequence the pressure on fast-food kitchens has also increased, leading to longer wait times and more mistakes in orders.

 Getting an order wrong or making a mistake in food preparation can lead to significant amounts of lost business over time, as customers begin to lose confidence in the quality of the food they are being served. Therefore improving the accuracy of order preparation is vital for both customer satisfaction and business success.

Monitoring food preparation with computer vision

 Computer vision based AI models can play an important part in reducing errors in order preparation. Object recognition allows AI systems to identify individual ingredients and know what a correctly assembled food item should look like.

 Overhead cameras can be installed in restaurants to monitor how food preparation is occurring. Computer vision capabilities allow these cameras to catch mistakes before workers deliver them to the customer. This technology can also improve food preparation efficiency by instructing workers on the most effective ways to assemble any given menu item.

 In short, by reducing wait times and improving order accuracy AI powered cameras can give customers a more satisfying experience when they order fast-food.

How outsourcing data annotation could accelerate development in this sector

 For food preparation monitoring technology to function well developers need to train models with annotated images and video. However, data annotation can be difficult for AI companies to manage. Crowd-sourced annotations can often be lower quality, which affects the performance of final models.

 Keymakr is a data annotation company with a dedicated in-house annotation team. This quality focused structure offers key advantages to AI developers in the restaurant sector:

• Semantic segmentation: This annotation method separates and assigns every pixel in an image to a particular label. As a result this method creates more detailed training information for AI models. Keymakr specializes in producing accurate semantic segmentation for every industry.
• Quality verification: Accurate annotations are essential for system performance. Therefore it is vital that annotation services have robust quality verification processes. Keymakr uses three layers of human cross checking to verify annotations. In addition Keymakr uses an auto-quality control script to catch any remaining errors.
• Proprietary technology: AI companies get access to a powerful annotation platform and tools by partnering with Keymakr. This proprietary technology boasts unique project management and worker analytics capabilities.
  
   It is also designed to improve work sharing, with features allowing multiple annotators to work on one training video at the same time.