LAPSE:2023.3216v1
Published Article
LAPSE:2023.3216v1
A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning
February 22, 2023
Abstract
Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.
Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.
Record ID
Keywords
deep learning, edge computing, Machine Learning, open source unmanned aerial vehicle, plastic waste detection, remote sensing, water environment protection
Subject
Suggested Citation
Luo W, Han W, Fu P, Wang H, Zhao Y, Liu K, Liu Y, Zhao Z, Zhu M, Xu R, Wei G. A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning. (2023). LAPSE:2023.3216v1
Author Affiliations
Luo W: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o [ORCID]
Han W: North China Institute of Aerospace Engineering, Langfang 065000, China
Fu P: Key Laboratory of Advanced Motion Control, Fujian Provincial Education Department, Minjiang University, Fuzhou 350108, China
Wang H: North China Institute of Aerospace Engineering, Langfang 065000, China
Zhao Y: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Liu K: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Liu Y: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Zhao Z: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Zhu M: North China Institute of Aerospace Engineering, Langfang 065000, China
Xu R: North China Institute of Aerospace Engineering, Langfang 065000, China
Wei G: North China Institute of Aerospace Engineering, Langfang 065000, China
Han W: North China Institute of Aerospace Engineering, Langfang 065000, China
Fu P: Key Laboratory of Advanced Motion Control, Fujian Provincial Education Department, Minjiang University, Fuzhou 350108, China
Wang H: North China Institute of Aerospace Engineering, Langfang 065000, China
Zhao Y: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Liu K: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Liu Y: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Zhao Z: North China Institute of Aerospace Engineering, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; National Joint Engineering Research Center o
Zhu M: North China Institute of Aerospace Engineering, Langfang 065000, China
Xu R: North China Institute of Aerospace Engineering, Langfang 065000, China
Wei G: North China Institute of Aerospace Engineering, Langfang 065000, China
Journal Name
Processes
Volume
10
Issue
1
First Page
131
Year
2022
Publication Date
2022-01-10
ISSN
2227-9717
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Original Submission
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PII: pr10010131, Publication Type: Journal Article
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LAPSE:2023.3216v1
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https://doi.org/10.3390/pr10010131
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[v1] (Original Submission)
Feb 22, 2023
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