International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 55–58, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-55-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 55–58, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-55-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Aug 2019

20 Aug 2019

DETECTION AND MONITORING OF BEACH LITTER USING UAV IMAGE AND DEEP NEURAL NETWORK

S. H. Bak, D. H. Hwang, H. M. Kim, and H. J. Yoon S. H. Bak et al.
  • Pukyong National University, Division of Earth Environmental System Science, 48513 Nam-gu Busan, South Korea

Keywords: Marine Debris, Unmanned Aerial Vehicle, Neural Network, Deep Learning, Marine Pollution

Abstract. Beach litter destroys marine ecosystems and creates aesthetic discomfort that lowers the value of the beach. In order to solve this beach litter problem, it is necessary to study the generation and distribution pattern of waste and the cause of the inflow. However, the data for the study are only sample data collected in some areas of the beach. Also, most of the data covers only the total amount of beach litter. UAV(Unmanned Aerial Vehicle) and Deep Neural Network can be effectively used to detect and monitor beach litter. Using UAV, it is possible to easily survey the entire beach. The Deep Neural Network can also identify the type of coastal litter. Therefore, using UAV and Deep Neural Network, it is possible to acquire spatial information by type of beach litter.

This paper proposes a Beach litter detection algorithm based on UAV and Deep Neural Network and a Beach litter monitoring process using it. It also offers optimal shooting altitude and film duplication to detect small beach litter such as plastic bottles and styrofoam pieces found on the beach.

In this study, DJI Mavic 2 Pro was used. The camera on the UAV is a 1-inch CMOS with a resolution of 20MP. The images obtained through UAV are produced as orthoimages and input into a pre-trained neural network algorithm. The Deep Neural Network used for Beach litter detection removed the Fully Connected Layer from the Convolutional Neural Network for semantic segmentation.