Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle
| Autor: | Krzysztof Walas, Bartosz Ptak, Mateusz Piechocki, Marek Kraft |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning UAV Science Real-time computing deep learning Image processing Global Map object detection trash Convolutional neural network Object detection Task (project management) image processing litter Software deployment General Earth and Planetary Sciences Artificial intelligence business YOLO |
| Zdroj: | Remote Sensing; Volume 13; Issue 5; Pages: 965 Remote Sensing, Vol 13, Iss 965, p 965 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13050965 |
| Popis: | Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning. |
| Databáze: | OpenAIRE |
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