Managing waste and recycling is an important emerging application for machine learning. A 2018 study by the World Bank predicted that global annual waste generation will rise to 3.4 billion tonnes over the next 30 years, up from 2.01 billion tonnes in 2016. This ever rising waste burden has increased pressure on business and governments to find solutions and promote recycling efforts. AI systems have the potential to make waste management safer, more efficient, and less harmful to the environment. However, there are significant challenges that need to be overcome on the way to achieving this goal.
This blog will focus on four core issues facing waste management AI developers. Image and video annotation services, like Keymakr, are collaborating with industry leaders to ensure that they receive the quality datasets that their projects need. Data collection and annotation providers are playing an important role in securing the future of this vital technology.
Identifying dangerous waste
Medical waste and biohazardous waste can be a significant danger to human operators. Similarly toxic materials from industry can pose a risk if not handled correctly. These potential harms could be significantly reduced if computer vision AI powered robots were reliably able to identify hazardous materials and process them without human intervention. In order to achieve this AI models need to be able to identify dangerous waste one hundred percent of the time.
Quality control is essential in data annotation when lives are at stake. Keymakr ensures that mistakes do not make it to the final dataset by employing multiple levels of quality control and cross checking. This means that training images for AI will also correctly locate and label hazardous materials.
Waste management AI models must be capable of identifying different types of recyclable material and categorising them accordingly. Many types of packaging or waste materials are now identified with specific markings to indicate what they are and how they should be processed. Automated systems can read these markings and then sort waste based on that information.
Accurate annotation is essential for making sure that models can reliably interpret identification markings. These specific shapes and numbers can be precisely highlighted in training images, allowing algorithms to learn from thousands of examples.
Sorting materials for recycling
Sorting facilities for waste are being asked to process larger amounts of material each year. AI assisted sorting promises to alleviate some of this burden by making identifying and categorising waste more efficient. This technology can either help to analyse the work of human operators in order to make processes more efficient, or can be incorporated into industrial robotics to automate some aspects of sorting.
Waste recognition is made possible with meticulously annotated video and image data. Keymakr makes use of semantic segmentation annotation to divide training images into specific classes of waste objects, pixel-by-pixel. This helps the model to discern between categories of waste and act accordingly.
Surveying waste with drones
Landfill areas can be particularly difficult to survey and analyse thanks to the wide areas they cover and the complexity of the accumulated waste. Drones, fitted with computer vision based AI models, are being deployed to find specific types of waste over large distances. This technology also has the ability to find areas of plastic waste in our oceans, helping to protect the food chain and marine wildlife.
Diverse video training data is key for the development of drone technology. Footage taken in a variety of locations and in a variety of conditions (light, weather) is most able to reflect the complexity of real world environments.
Quality annotation facilitates successful waste management AI development
Keymakr’s teams of experienced annotators can provide innovation supporting annotation services at a competitive price. Contact a team member to book your personalized demo today.