지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Purpose: This study is to analyze the image classification using Convolution Neural Network and Transfer Learning for Jeju Island and to suggest related implications. As the biggest tourist destination in Korea, Jeju Island encounters environmental issues frequently caused by marine debris along the seaside. The ever-increasing volume of plastic waste requires multidirectional management and protection. Research design, data and methodology: In this study, the deep learning CNN algorithm was used to train a number of images from Jeju clean and polluted beaches. In the process of validating and testing pre-processed images, we attempted to explore their applicability to coastal tourism applications through probabilities of classifying images and predicting clean shores. Results: We transformed and augmented 194 small image dataset into 3,880 image data. The results of the pre-trained test set were 85%, 70% and 86%, and then its accuracy has increased through the process. We finally obtained a rapid convergence of 97.73% and 100% (20/20) in the actual training and validation sets. Conclusions: The tested algorithms are expected to implement in applications for tourism service distribution aimed at reducing coastal waste or in CCTVs as a detector or indicator for residents and tourists to protect clean beaches on Jeju Island.
목차
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참고문헌 (56)
참고문헌 신청
Aall, C. / 2019 / The Discourse on Sustainable Urban Tourism : The Need for Discussing More Than Overtourism / Sustainability 11(15):1~12
Abbasi, M. / 2016 / Forecasting municipal solid waste generation using artificial intelligence modelling approaches / Waste management 56:13~22
Agarap, A. F. / 2018 / Deep learning using rectified linear units(relu) / arXiv preprint arXiv: 1803.08375
Alkalay, R. / 2007 / Clean-coast index - A new approach for beach cleanliness assessment / Ocean & Coastal Management 50(5-6):352~362
Arora, R. / 2016 / Understanding deep neural networks with rectified linear Units / arXiv preprint arXiv:1611.01491
Abbasi, M. / 2016 / Forecasting municipal solid waste generation using artificial intelligence modelling approaches / Waste management 56:13~22
Agarap, A. F. / 2018 / Deep learning using rectified linear units(relu) / arXiv preprint arXiv: 1803.08375
Alkalay, R. / 2007 / Clean-coast index - A new approach for beach cleanliness assessment / Ocean & Coastal Management 50(5-6):352~362
Arora, R. / 2016 / Understanding deep neural networks with rectified linear Units / arXiv preprint arXiv:1611.01491
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