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題名 Enhancing green sea turtle (Chelonia mydas) conservation for tourists at Little Liuqiu island, Taiwan: Application of deep learning algorithms. 作者 陳楊文
Chen, Vincent Y.;Wu, Ya-Wen;Hu, Chih-Wei;Han, Yu-San貢獻者 地政系 關鍵詞 Sightings; Deep learning; Green sea turtle; Conservation initiative 日期 2024-06 上傳時間 10-Sep-2024 13:20:33 (UTC+8) 摘要 Observing marine life has emerged as a pivotal catalyst for the growth of the Blue Economy. Yet, overzealous and recurrent observations may exert undue stress on marine creatures, thereby complicating the sustainable management of marine assets. After the easing of Covid-19 pandemic restrictions, a notable influx of tourists to Little Liuqiu Island, Taiwan puts considerable stress on its green sea turtle population. This surge intensified incidents of illegal harassment, triggering concerns from both the local community and administrative bodies. The prevailing challenge is to ensure tourists observe these turtles with respect, refraining from behaviors such as touching or pursuing them. Addressing this, our study harnesses deep learning algorithms to equip ecotourism operators and tourists with tools to detect green sea turtles across diverse coastal terrains, reinforcing conservation efforts. Our analysis scrutinized object detection AI models, namely YOLOv3, YOLOv5s, and YOLOv5l. Fieldwork was undertaken on the island to gather ample training images, capturing elusive green sea turtles in various settings, from coastline strolls to drone imagery. Supplemental images sourced from local social media platforms were later added. Contrary to expectations, we found that merely expanding the training dataset did not guarantee improved outcomes. Instead, the variance in image content, considering distances, angles, and turtle appearances, played a pivotal role in enhancing model precision. Through our experimentation, the streamlined YOLOv5s model consistently eclipsed its more complex counterparts in performance. An AI service, underpinned by the YOLOv5s model, has been launched to distinguish between green sea turtle types for tourist-focused conservation initiatives. Future iterations will incorporate user feedback to refine accuracy. Our research breaks new ground, spotlighting the intricacies of gathering natural environment data, pinpointing optimal AI models, and evaluating their practical implications for green sea turtle conservation. 關聯 Ocean & Coastal Management, Vol.252, 107111 資料類型 article DOI https://doi.org/10.1016/j.ocecoaman.2024.107111 dc.contributor 地政系 dc.creator (作者) 陳楊文 dc.creator (作者) Chen, Vincent Y.;Wu, Ya-Wen;Hu, Chih-Wei;Han, Yu-San dc.date (日期) 2024-06 dc.date.accessioned 10-Sep-2024 13:20:33 (UTC+8) - dc.date.available 10-Sep-2024 13:20:33 (UTC+8) - dc.date.issued (上傳時間) 10-Sep-2024 13:20:33 (UTC+8) - dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153680 - dc.description.abstract (摘要) Observing marine life has emerged as a pivotal catalyst for the growth of the Blue Economy. Yet, overzealous and recurrent observations may exert undue stress on marine creatures, thereby complicating the sustainable management of marine assets. After the easing of Covid-19 pandemic restrictions, a notable influx of tourists to Little Liuqiu Island, Taiwan puts considerable stress on its green sea turtle population. This surge intensified incidents of illegal harassment, triggering concerns from both the local community and administrative bodies. The prevailing challenge is to ensure tourists observe these turtles with respect, refraining from behaviors such as touching or pursuing them. Addressing this, our study harnesses deep learning algorithms to equip ecotourism operators and tourists with tools to detect green sea turtles across diverse coastal terrains, reinforcing conservation efforts. Our analysis scrutinized object detection AI models, namely YOLOv3, YOLOv5s, and YOLOv5l. Fieldwork was undertaken on the island to gather ample training images, capturing elusive green sea turtles in various settings, from coastline strolls to drone imagery. Supplemental images sourced from local social media platforms were later added. Contrary to expectations, we found that merely expanding the training dataset did not guarantee improved outcomes. Instead, the variance in image content, considering distances, angles, and turtle appearances, played a pivotal role in enhancing model precision. Through our experimentation, the streamlined YOLOv5s model consistently eclipsed its more complex counterparts in performance. An AI service, underpinned by the YOLOv5s model, has been launched to distinguish between green sea turtle types for tourist-focused conservation initiatives. Future iterations will incorporate user feedback to refine accuracy. Our research breaks new ground, spotlighting the intricacies of gathering natural environment data, pinpointing optimal AI models, and evaluating their practical implications for green sea turtle conservation. dc.format.extent 111 bytes - dc.format.mimetype text/html - dc.relation (關聯) Ocean & Coastal Management, Vol.252, 107111 dc.subject (關鍵詞) Sightings; Deep learning; Green sea turtle; Conservation initiative dc.title (題名) Enhancing green sea turtle (Chelonia mydas) conservation for tourists at Little Liuqiu island, Taiwan: Application of deep learning algorithms. dc.type (資料類型) article dc.identifier.doi (DOI) 10.1016/j.ocecoaman.2024.107111 dc.doi.uri (DOI) https://doi.org/10.1016/j.ocecoaman.2024.107111
