Modern cities are huge, complex and hard to manage. City governments and businesses have a responsibility to keep the built environment safe, easy to navigate and clean. The desire to improve urban living has led to the concept of the smart city. Using cameras and AI algorithms governments can autonomously monitor many different aspects of a city.
As a result, services are more responsive for citizens without the need to increase budgets substantially. Efficient, smart cities are made possible by data annotation. Data annotation helps computer vision algorithms to learn by adding information to images and videos. Image and video annotation specialists, like Keymakr, can support smart vision innovators with precise data labeling.
This blog will focus on one specific smart city computer vision application, waste monitoring. Firstly we will look at why illegal waste dumping is such a challenge for cities. Secondly, we will show how AI can help overcome this issue. Then, we will identify the key annotation techniques used by developers in this sector. And finally, we will identify the advantages offered by data annotation providers.
A problem for cities
Littering and illegal waste dumping pose a serious problem for city authorities. Improper disposal of gardening waste, building materials and old furniture can be a nuisance for local residents, making areas unsightly. In addition illegal dumping makes access an issue as older people and children can find it hard to navigate around large waste objects dumped on the street.
Other kinds of waste can be actively dangerous. Electrical devices, broken machinery, used tyres, old batteries and chemical products are a real risk if they are not disposed of correctly. Despite the issues caused by illegal waste dumping it is very difficult for city authorities to maintain round the clock monitoring and enforcement. There are simply not enough staff to check cameras and deter offenders.
Computer vision monitoring
AI powered camera systems can help cities to combat illegal waste dumping. Using object recognition capabilities computer vision street cameras can identify waste objects that are cluttering up sidewalks and roads. Then, the system can autonomously alert city services.
This means that workers can be quickly dispatched to clear the waste. Street level cameras may also be able to capture number plates used by dumpers, allowing fines to be issued and acting as a deterrent.
The right annotation techniques
Automated waste monitoring is made possible by image and video annotation. The following techniques ensure that computer vision systems perform reliably in the streets:
- Polygon annotation: This annotation technique is used to capture irregular shapes in images. Waste objects come in all shapes and sizes. As a result, annotators use small lines connected with vertices to trace the shape of objects in digital images. This technique is also essential for semantic segmentation.
- Video annotation: Annotated video helps AI algorithms to understand movement in a dynamic world. Annotators use tools to label each frame of video training footage. This process is time consuming so many annotation platforms use object interpolation features to speed up object location.
- Lane annotation: Correctly identifying the shape of streets and roads is useful for computer vision models trying to work out if waste has been dumped incorrectly. Lane annotation adds lines to images and frames to show the structure of linear objects like roads and railways.
Annotation services help developers
Image and video annotation can be an expensive distraction for smart city AI developers. Keymakr supports AI innovation with dedicated in-house annotation teams, unique workforce analytics options, and a robust verification system.