Abstract | ||
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Floating trash in the river presents a major environmental problem that endangers the lives of river inhabitants. However, conventional trash monitoring relies on labor-intensive manual inspection by dispatching inspectors to the filed, which is highly cost-ineffective. Despite that Unmanned Aerial Vehicles (UAVs) have been widely proposed for many real-time monitoring applications, limited power supply remains the most challenging bottleneck for such UAV-assisted monitoring applications. In this work, we propose an automated river trash monitoring system called SuperDock. SuperDock consists of a remote processing unit, a docking station and a UAV. SuperDock lands the UAV precisely onto the docking station followed by performing automated battery replacement. As a result, the UAV can continue the monitoring task after the battery replacement. Furthermore, SuperDock includes a deep learning-assisted river trash detection module based on YOLOv3 that runs much faster than the conventional Convolution Neural Networks (CNN). In addition, a data set has been generated for training and testing the deep learning network, specifically for the floating trash detection application. Finally, SuperDock enables the UAV to communicate wirelessly with a remote computer in a real-time manner. Experimental and simulation results show that SuperDock is highly effective in monitoring the floating trash in the river. |
Year | DOI | Venue |
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2019 | 10.1109/ROBIO49542.2019.8961509 | ROBIO |
Field | DocType | Citations |
Bottleneck,Monitoring system,Convolution,Docking station,Transfer of learning,Real-time computing,Control engineering,Artificial intelligence,Deep learning,Engineering,Battery (electricity),Artificial neural network | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Guanchong Niu | 1 | 0 | 0.34 |
Jie Li | 2 | 0 | 0.34 |
Sheng Guo | 3 | 0 | 0.34 |
Man-On Pun | 4 | 0 | 0.34 |
Leo Hou | 5 | 0 | 0.34 |
Lujian Yang | 6 | 0 | 0.34 |