| dc.contributor | 資訊系 | |
| dc.creator (作者) | 彭彥璁 | |
| dc.creator (作者) | Hung, Yi-Chieh;Chan, Jhih-Ya;Lien, Wei-Cheng;Peng, Yan-Tsung;Chen, Li-Shu | |
| dc.date (日期) | 2026-03 | |
| dc.date.accessioned | 8-Sep-2026 15:06:33 (UTC+8) | - |
| dc.date.available | 8-Sep-2026 15:06:33 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-Sep-2026 15:06:33 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=184853 | - |
| dc.description.abstract (摘要) | The sea turtle is an indicator organism used in marine conservation to identify the health status of ecosystems in various marine regions. In the past, researchers had to review an 8 h underwater video every day to monitor and count sea turtle appearances. However, since sea turtles often appear for only short periods, traditional approaches of manual searching and counting require significant labor and time to ensure accurate periods of their appearance. To address this issue, we adopted the You Only Look Once (YOLO) model for object detection, utilizing real underwater videos captured from three different areas in the Taiwan Keelung City Chaojing Bay Aquatic Plants and Animals Conservation Area for training and testing. To overcome limitations, such as underwater blur, sediment interference, obstructions from other fish, and distant targets that are challenging to identify, we applied data augmentation techniques, including scaling, rotation, and depth blur, with labeled data of different fish species to improve generalization capability. The experimental results of this study showed that this method achieves a 99.4% accuracy in sea turtle detection. After 60 days of deployment across the three areas, the model reduced search time by over 99%, significantly improving efficiency and reducing workload. | |
| dc.format.extent | 105 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | The 2025 IEEE International Conference on Computation, Big-Data and Engineering (ICCBE), TAR UMT; IEEE; National Penghu University of Science and Technology, pp.1-10 | |
| dc.subject (關鍵詞) | marine conservation; AI; sea turtle recognition; data augmentation | |
| dc.title (題名) | Sea Turtle Recognition with Multiple Data Augmentation Methods Suitable for Marine Scenarios | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.3390/engproc2026128011 | |
| dc.doi.uri (DOI) | https://doi.org/10.3390/engproc2026128011 | |
