The management of waste is an often unseen but essential task that ensures our communities, businesses and hospitals can function safely. Sorting, extracting and disposing of waste is a difficult and dirty job that can expose workers to potentially dangerous substances. Computer vision based AI models are beginning to be deployed in this industry with the hope of making waste management more efficient and safer.
Many of these models are built with the help of video annotation. Video annotation primarily involves human annotators locating, outlining and labeling objects in every frame of a piece of footage. This information is then used to train waste management AI systems.
This blog will highlight some of the promising AI use cases currently in development in the waste management sector. We will then look at the challenges that video annotation can pose to developers, and show how outsourcing to video annotation specialists can help overcome them.
Waste management AI applications
Advances in machine learning have precipitated the creation of a new generation of computer vision technologies in the waste management industry:
- Medical and biohazard waste: Hospitals are among the institutions that create biohazard waste. This material is by definition a threat to the humans that may have to sort and process it. AI systems, trained with video annotation, can improve the handling of biohazard waste. Autonomous robots, powered by AI, could take over the majority of biohazard waste management, making the process far safer for workers.
- Marking recognition: Many waste material types feature markings that indicate if and how they can be recycled or processed. AI models can be trained to recognize these markings, potentially speeding up the recycling process and ensuring that less waste reaches the landfill.
- Drone surveillance: Drones can be used to monitor landfill sites from the air. AI systems installed on these machines can automatically locate waste, providing valuable information for site operators. Drones fitted with this technology can also be used to look for plastic waste in the ocean. They are able to survey large areas and spot waste that humans might miss.
- Sorting facilities: Automated waste sorting will improve waste management efficiency and accuracy. Object recognition AI models, combined with robotics, are capable of processing huge volumes of waste precisely and tirelessly.
Video annotation challenges
The applications detailed above rely on video annotation for their continued development. However, it can be difficult for AI companies to manage large video annotation operations. Annotating thousands of frames per video is a challenge that crowdsourced or remote workers may not be able to meet.
In order to maintain accuracy and quality across video datasets many AI developers are turning to video annotation specialists, like Keymakr. Keymakr’s in-house team of annotators has experience executing large scale video annotation projects, and Keymakr’s unique annotation tool options are also designed with video annotation in mind.
Video annotation services can offer waste management AI innovators a number of specific advantages:
- Bounding boxes: This is the most commonly used annotation type, and involves using a box to locate objects in a video frame. Keymakr’s quality control procedures ensure that bounding boxes remain consistent across training footage.
- Polygon annotation: This annotation type is used to precisely locate complex and irregular shapes. Annotators trace the outline of objects with small lines connected at vertices. Keymakr’s task sharing options allows multiple annotators to apply this technique to the same piece of footage, making polygon annotation faster.
- Smart task distribution: Keymakr’s proprietary annotation platform tracks worker performance and other metrics. Using this information annotation tasks can be given to annotators who are best suited to completing them.
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