UAVVaste

Drone rubbish detection intelligent technology

About project

Using an unmanned aerial vehicle (UAV) equipped with a GPS locator and performing a flight along a given route (automatically or manually controlled), we locate abandoned waste in a given area. The extension of GPS data with a high-resolution vision system provides a highly accurate location. The observation from above and the ability to quick, unlimited movement of the UAV make the map obtained in a very short time. The designated waste location points can be transferred to a cleaning team or an autonomous robot, additionally planning their route so as to minimize cleaning time and workload.

Collection of data

We use the DJI Mavic Air as an Unmanned Aerial Vehicle that captures the image and provides sensor data.

Image annotation

Current algorithms need photos with annotations and the only option is to do them by a human.

Rubbish detection

We use object detection algorithms that are not only accurate but also computationally effective.

Signal processing

We process GPS data and use vision systems to improve accuracy.

Map of detected waste

We provide a detailed map with marked rubbish. Perfect for a cleaning robot!

filter_drama UAVVaste dataset

The UAVVaste dataset consists to date of 772 images and 3716 annotations. The main motivation for creation of the dataset was the lack of domain-specific data. The datasets that are widely used for object detection evaluation benchmarking. The dataset is made publicly available and is intended to be expanded.


book Publication

@Article{rs13050965,
 AUTHOR = {Kraft, Marek and Piechocki, Mateusz and Ptak, Bartosz and Walas, Krzysztof},
 TITLE = {Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle},
 JOURNAL = {Remote Sensing},
 VOLUME = {13},
 YEAR = {2021},
 NUMBER = {5},
 ARTICLE-NUMBER = {965},
 URL = {https://www.mdpi.com/2072-4292/13/5/965},
 ISSN = {2072-4292},
 DOI = {10.3390/rs13050965}
}


emoji_events Awards

Award in the category of Technological Innovation during Eco-Innovators 2021
3rd place on Teknofest 2021 in the Free UAV mission category, Yunuseli Airport Bursa
8th place on EKOinnowatorzy 2020 in the EKOinnovative Student Project category, online

Bartosz Ptak

Bartosz Ptak

PhD student at Poznan University of Technology, interested in GPUs, EDGE devices, deep learning and image processing.
bartptak@gmail.com
AKL
Mateusz Piechocki

Mateusz Piechocki

PhD student at Poznań University of Technology. Member of PUT Motorsport Driverless team. Interested in machine learning, robotics and computer vision.
piechocki.mateusz97@gmail.com