AI Superior GmbH

Summary: For a large semi-government organization, our team developed a system that detected litter objects from images captured by drones and helped to manage trash collection activities. We designed and developed a GIS-based application that allowed convenient interaction with detected litter objects and facilitated trash collection activities via optimal route planning and tracking of the collection progress.

Challenge: Tons of litter objects were dragged by waves to a coastal area, scattering them across a territory of around one thousand square km. Being dispersed across many single islands these objects were quite difficult and costly to identify by a human. Hence, it was required to design and implement an efficient strategy that allowed the detection and collection of litter.

Solution by AI Superior: We applied our proprietary computer vision technology for object detection, classification, and segmentation and designed and developed an interactive GIS-based application to display results and operate collection activities. The computer vision detection technology was applied to images captured by a drone. The drone flights were operated by a partnering team that provided RGB images covering the whole territory (the flight altitude was around 50 metres high above the land)

Outcome and Implications: the developed solution allowed to reduce the time required to perform trash detection activities by the factor of 25. This  resulted in significant cost savings, halving the overall detection and collection costs. Furthermore, the collection time decreased by a factor of 4 while the automated computer vision solution allowed a 7% higher detection accuracy compared to a human expert. Additionally, this system decreased carbon footprint by a factor of 19.

The picture shows the dashboard of the application.

Summary: Our team developed a drone-based application that was able to detect and report 25 different types of construction debris. The solution allowed to automatize the construction site inspection process while reducing human involvement and average inspection costs considerably.

Challenge: A city municipality requested a solution that allowed them to fully automate construction site compliance monitoring and detect abandoned construction debris such as bricks, cement blocks, sand heaps, metal and wooden sticks, etc. It was crucial to have an automated solution that would minimize human involvement in the inspection process thus reducing labor costs as well as the time required for an inspection.

Solution by AI Superior: We applied our proprietary computer vision technology for object detection, classification, and segmentation to detect 25 different classes of construction debris. We built a GIS dashboard to allow selection of a construction site and the visualization of all the debris detected within it. Additionally, for every detected object the system provided an estimated size (area) of construction debris (for a single object and clusters of objects of the same type) as well as the amount of detected objects. The application provided insights, while employing a GIS dashboard and exposed APIs to query detection results – this allowed the solution to be integrated to practically any other system.

Outcome and Implications: The solution was adopted by multiple city municipalities demonstrating its operational and economical effectiveness. Furthermore, according to our customer estimates, the system saved 320 man-hours per month and reduced average inspection costs by 40%.

The picture shows the result of the Construction debris detection from a drone. The different objects are dyed in different colors.

For a niche insurance company operating in a medical/health domain we developed a prediction machine learning-based model to estimate the risk of an economical loss. The machine learning model is based on neural networks and built by consuming historical medical data over five consec-utive years. The developed model significantly outperformed statistical approaches. With this mod-el, the customer was able to optimize its pricing policies which resulted in significant savings.