How trash object detection from a drone can halve the overall detection and collection cost
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.
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.
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.
It was required to design and implement an efficient strategy that allowed the detection and collection of litter. Therefore, drone infrastructure inspection came as a suitable solution.
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)
This image shows the dashboard from the application for drone object detection (Detections and GIS application)
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.
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