Winegrowing in Baden and the Palatinate is an integral part of the collective identity of these regions. In the coming years and decades, the economic fate of the viniculture will be shaped by climate change and its consequences like that of hardly any other economic sector. This complex problem includes the spread of certain bacterial and fungal diseases in the vineyard as well as research into resistance to those diseases. Thus, we decided on a use case in the field of disease control in vineyards. We set ourselves the task of using computer vision to enable reliable detection of important vine diseases and implementing this on an edge computer, the NVIDIA Jetson Nano. In viticulture, there are a number of different diseases that affect the vine and are partly responsible for quality or quantity losses in the harvest. The amount of recognizable, relevant diseases can be limited to two essential ones: Peronospora (Pero) and Esca disease.
Do we have a problem? In Germany, only 32 percent of what ends up in the residual waste actually belongs in the residual waste bin (Umweltbundesamt, 2020). Also in Karlsruhe, local waste management institutions face poorly separated trash bins, such as recycable material (Wertstoffe) which can contain up to 50% of residual waste (Restmüll) according to Amt für Abfallwirtschaft Karlsruhe. Recyclable materials are recycled and reused, while residual waste is usually incinerated, exported to developing countries or otherwise disposed of, which leads to environmental pollution. Current endeavours to improve the quality of trash separation, like information sheets of public authorities, technical articles lack the desired effect possibly because they appear not to be focused on the citizens enough.
In most modern plastic waste separation plants, trash gets segregated using high-tech sensors with high detection rates of different plastic types. However, the technology fails on black plastic and plastic that is covered with a “stretch foil” because the plastic has the same black color as the conveyor belt on which the plastic runs, or it cannot be detected since the plastic is hidden behind the foil. This may result in a quality loss of the recycled end product. What is an impossible task for the sensors, we aim to solve using Computer Vision.
Grocery Vision is a computer vision application that simplifies the grocery checkout process. What does "simpler" mean? By simpler, we mean that users don't have to scan labels or memorize PLU codes.
With the ever-growing popularity of smartphones and the internet, more and more people record videos in public and even share live streams on social media. We protect the privacy of bystanders, who cannot consent to be recorded by automatically blurring their faces in real-time. We achieve this by employing AI, not to automatically collect personal data like it is done in autocratic regimes, but to help people to navigate public places anonymously. Our user base is anyone who shares videos such as interviews for TV channels like ARD or ZDF or social media platforms like Instagram.
In July 2022, the Statistisches Bundesamt published main causes for driver misbehavior in road traffic accidents with personal injury in Germany from 2010 to 2021. Unfortunately, the shares of driver misbehavior in road traffic have not changed significantly since 2010. Especially, misbehavior in turning, right of way, speed, wrong road use and distance have been main causes for accidents involving drivers in road traffic with personal injury since 2010.
RottenApricot is a service to identify apricots and categorize them in “good” apricots, that can be sold to the customer, and “bad” apricots, that are damaged, rotten or overripe.
The Quick Service Restaurant Order Assistant is an easy-to-implement, additional verification instance for quick service restaurants capable of significantly reducing costs caused by human failure in the processing of orders.
With the goal of providing assistance to visually impaired people, we developed a real-time stair recognition system. During the summer term 2020, we successfully developed this computer vision prototype from end to end, from ideation to image retrieval and model training to deployment on a Jetson Nano.