Instance Segmentation for Waste Management AI

Jul 27, 2021

Waste management is a vital, and often underappreciated, industry. Careful sorting, disposal, and recycling of waste materials can protect the environment, improve public health, and increase sustainability. The pressure on these services has grown significantly in recent years, due to increasing consumer demand and growing concerns about plastic waste. In order to meet these challenges this essential sector is beginning to turn to AI technology, increasing efficiency and improving worker safety in the process.

Computer vision based models are promising to streamline waste management systems in a variety of ways. However, in order for this technology to reach its potential developers need access to precise, annotated training data. This blog will focus on a specific annotation technique: instance segmentation. We will define this technique, show how it is being used, and finally suggest how waste management innovators can secure their data needs by using professional annotation providers.

What is instance segmentation?

Semantic segmentation assigns each pixel in an image to a real world category. In an image of street traffic all cars would be outlined and colour coded. Instance segmentation creates additional granularity in training data by separating each occurrence of a particular object. In the street traffic image this would mean that each outlined car would have its own colour identifying it.

Waste management use cases supported by instance segmentation

Instance segmentation can allow waste management AI systems to learn how to differentiate between types of waste, as well as recognising individual waste items. Annotated training imagery and video supports a number of interesting AI use cases:

  • Waste sorting facilities: Sorting facilities can be transformed by the introduction of robotic waste sorting systems. These devices can dramatically increase the efficiency of waste management operations, whilst keeping workers safe. Instance segmentation is part of the development of the machine learning models that power these machines.
Video annotation | Keymakr
  • Landfill and ocean surveying: Drones can be used to survey large areas of landfill or even ocean. These searches can find potentially harmful plastic waste, or target other waste types as necessary. Training data annotation means that these drones can automatically scan video imagery and act as a force multiplier for clean up efforts.
Image annotation | Keymakr
  • Managing medical and biohazard waste: Safety should be the top priority when managing potential harmful waste. Robots can help to minimise the amount of human handling at sorting facilities. These machines can target dangerous objects, such as syringes, with the help of instance segmentation.
Instance segmentation

How to ensure quality instance segmentation annotation

Instance segmentation is at the core of AI training for a wide range of promising applications in the waste management sector. It is vital that developers in this industry maximise the quality of their annotated data. In order to achieve this many companies are taking advantage of the services offered by professional annotation providers, like Keymakr:

  • Workflow: It can be challenging to coordinate labeling and verification across a large data annotation project. Outsourcing to annotation providers can allow developers to access proprietary annotation platforms that streamline workflows ensuring accurate and fast data annotation.
  • Precision: Crowdsourced annotation can offer benefits in terms of cost but it can also mean compromising on precision in training datasets. Keymakr operates an in-house annotation staff, overseen by experienced managers. Centrally located and well managed teams can guarantee precision in training images and video annotation.
  • Communication: Distributed annotation workforces can often be difficult to communicate with. Time zones, access to the internet, cultural differences, are just some of the impediments to communication that come from crowdsourcing. Managed teams, like those at Keymakr, are much easier to relay needs and troubleshooting to. This increased responsiveness ensures quality and flexibility.
Keymakr Demo

Keymakr leverages skilled annotation teams, experienced managers, and innovative tools to provide fast annotation services to AI leaders.

Inna Nomerovska

Inna Nomerovska is the VP of Marketing and Brand Strategy at Keymakr | Keylabs. She is a tireless technology enthusiast with 15 years of experience in international marketing and startup background.

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