• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.1
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• pp.78-86
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• 2013

In order to analyze the pollution extent of small plastic debris(SPD) ashore on the shoreline of coastal flow, 12 of survey was conducted at Song-Jung beach of Busan for several years. The sampled beach was divided into 9 sites with unit area($m^2$). Many of SPD were detected in the southern part of the beach and classified into 11 items as P1 to P11 according to the contents. Average densities of total items' weight were $2.955g/m^2$ and weights of P2, P3 item were composed of about 64% among them. And average densities of total items' quantity were $56.259ea/m^2$ and quantities of P6 only were composed of about 63%. Seeing the seasonal variation, fall season was abundant extremely whereas nearly nothing in spring. The correlation of weights and quantities have reliable coefficients to some extent on sites and season but nearly don't have reliances on item, tide, wind and precipitation. Many researching data were required if possible in order to discussing about the correlation.