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研究生:蘇立忻
研究生(外文):SU,LI-HSIN
論文名稱:基於深度學習之魚類辨識
論文名稱(外文):Fish identification based on deep learning
指導教授:許巍嚴
指導教授(外文):HSU,WEI-YEN
口試委員:黃冠華張怡秋
口試委員(外文):Huang, Guan-HuaChang, I-Chiu
口試日期:2024-01-08
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊管理學系碩士在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:49
中文關鍵詞:深度學習YOLO魚類辨識永續發展
外文關鍵詞:Deep LearningYOLOFish RecognitionSustainable Development
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  聯合國於西元 2015 年通過 2030 永續發展議程,提出 17 項全球邁向永續發展的核心目標,其中第 14 項目標 - 保育海洋與海洋資源,為實現環境目標與保持生物多樣性,聯合國糧農組織推動創新、包容、有效、有適用性的漁業管理系統( FAO , 2022 ),目標指標 14.4.1 計算在生物可持續水平範圍內的魚類種群數量,有助於評估全球在漁業政策與管理效性與效率,如何取得足夠資料和正確資料是當務之急。
  FAO 致力於推動攝像機器與深度學習的結合,用於管理和維護漁業價值鏈,本研究的目的為討辨識魚類最佳化模型,致力於提高辨識度與效率,為協助提供準確魚類資源數據。
  本研究針對近年卷機神經網路 YOLO 不同版本 — 2022 年推出的 YOLO v7與 2023 年推出的 YOLO v8 於魚類辨識之最佳化模型,探討不同 YOLO 模型於相同魚類樣本上之成效,進而延伸其他魚類或物種,結合專家人士進行科別標籤,區別魚類科別(如雅羅魚亞科、鯉科鯉亞科等)作為魚類相關知識介紹用途;實務上可進行相關性應用,如自動化辨識系統、魚類追蹤系統等,減少人工判斷與分類的人力,以提升整理效率;若能收集到保育魚類相關資料,進而做出辨識系統,除了能降低誤捕的風險之外,也能提高保育魚類後續追蹤效率,朝永續發展( SDGs )目標 14 - 保育海洋與海洋資源,為指標 14.2.3 建立海洋資料庫、指標 14.4.2 有效監管採收、消除過度漁撈、以及非法、未報告及不受規範( IUU )漁撈行為邁進。

關鍵字:深度學習、YOLO、魚類辨識、永續發展
  The United Nations adopted the 2030 Agenda for Sustainable Development in 2015, outlining 17 core global goals. Among these, Goal 14 focuses on the conservation of the oceans and marine resources. To achieve environmental goals and preserve biodiversity, the Food and Agriculture Organization (FAO) of the United Nations advocates for innovative, inclusive, effective, and adaptable fisheries management systems (FAO, 2022). Target indicator 14.4.1 calculates the population of fish species within biologically sustainable levels, aiding in assessing global effectiveness and efficiency in fisheries policy and management. Obtaining sufficient and accurate data is crucial in this context.
  The FAO is dedicated to promoting the integration of cameras and deep learning for managing and maintaining the fisheries value chain. The purpose of this study is to develop an optimized model for fish species recognition, aiming to enhance accuracy and efficiency to assist in providing accurate fish resource data.
  This research focuses on the optimization models for fish recognition using different versions of the YOLO convolutional neural network—specifically, YOLO v7 released in 2022 and YOLO v8 released in 2023. The study explores the effectiveness of different YOLO models on the same fish samples, extending to other fish species or organisms. Collaboration with experts for taxonomic labeling aims to distinguish fish categories (such as subfamilies like Yarrellinae or carp subfamily) for the purpose of introducing fish-related knowledge. Practical applications include automated identification systems, fish tracking systems, and other relevant applications, reducing the need for manual judgment and classification to improve efficiency.
  If conservation-related fish data can be collected, developing an identification system not only reduces the risk of bycatch but also enhances the efficiency of tracking for conservation purposes. This aligns with Sustainable Development Goals (SDGs), specifically SDG 14—Conserve and sustainably use the oceans, seas, and vmarine resources. This contributes to achieving indicators 14.2.3 (establishing marine databases) and 14.4.2 (effective monitoring of harvesting, elimination of overfishing, and regulation of illegal, unreported, and unregulated (IUU) fishing activities).

Keywords: Deep Learning, YOLO, Fish Recognition, Sustainable Development
誌謝 ii
摘要 iii
Abstract iv
目錄 vi
圖目錄 viii
表目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 預期成果與貢獻 4
第二章 文獻探討 7
2.1 機器學習與深度學習歷史演進 7
2.2 YOLO發展 9
2.3 深度學習於魚類辨識相關文獻 10
第三章 材料與研究方法 15
3.1 實驗材料 15
3.2 影像標註工具 16
3.3 方法流程圖 17
3.4 神經網路模型 18
3.5 深度學習處理流程 20
3.6 超參數設置 20
第四章 實驗結果與分析 21
4.1 實驗環境 21
4.2 實驗評估與指標 21
4.2.1 召回率 23
4.2.2 精準度 23
4.2.3 準確率 23
4.2.4 F1_Score 23
4.3 實驗結果 24
4.3.1 YOLO v7 模型結果 24
4.3.2 YOLO v8 模型結果 25
4.3.3 比較YOLO v7 與 YOLO v8 模型結果 27
第五章 結論 28
5.1 結論 28
5.2 研究限制 29
5.3 未來研究方向 29
參考文獻 30
中文文獻 30
英文文獻 32

中文文獻
孫梓鈞 (2014)。使用深度CNN辨識蔬果[碩士論文]。國立暨南國際大學資訊工程學系。
謝昕穎 (2019)。基於卷積神經網路作深度學習之魚類辨識系統[碩士論文]。國立台灣海洋大學電機工程學系。
杜柏宏 (2019)。訓練資料集自我擴增問題之分析 – 以YOLO分類器為例[碩士論文]。國立中山大學。
盧彥廷 (2019)。基於YOLO物體偵測進行即時的實力分割[碩士論文]。國立交通大學
張凱勳 (2019)。植基於深度學習之影像辨識技術之研究[碩士論文]。國立虎尾科技大學資訊工程系。
王靖惠 (2020)。使用深度學習於檢測番茄病害[碩士論文]。國立中正大學資訊管理學系。
呂秉諺 (2020)。基於卷積神經網路作深度學習之即時觀賞魚類辨識系統[碩士論文]。國立台灣大學電機工程學系。
楊立宏 (2021)。基於蘭嶼海洋文化加值應用之魚類影像智慧比對辨識技術[碩士論文]。靜宜大學資訊傳播工程學系。
黃郁宸 (2022)。應用深度學習於綠葉蔬菜辨識之研究[碩士論文]。國立中正大學資訊管理學系。
陳柏瑞 (2022)。應用深度學習物件偵測技術於水上偵測與搜救之研究[碩士論文]。國立中正大學資訊管理學系。
廖富民 (2022)。結合權重融合的多深度學習卷積神經網路於芒果等級分類之研究[碩士論文]。國立中正大學資訊管理學系。
黃馨儀 (2023)。基於深度學習模型應用於蘭花品種辨識[碩士論文]。國立中正大學資訊管理學系。
陳俊安 (2023)。應用深度學習於辨識葡萄樹葉病害之研究[碩士論文]。國立中正大學資訊管理學系。
曾定章 (2021)。影像處理/深度學習[課程講義]。國立中央大學資訊工程系。
文淵閣工作室 (2020)。Python機器學學習超進化:AI影像辨識跨界應用實戰(初版)。基峰資訊股份有限公司。
鄭建彥 (2019)。「建立自己的YOLO辨識模型 – 以柑橘辨識為例」。AI 人工智慧
https://blog.cavedu.com/2019/07/25/yolo-identification-model/
邵廣昭 (2022)。台灣海洋生物多樣性的現况和挑戰。國立台灣海洋大學海洋生物研究所
https://agritech-foresight.atri.org.tw/article/contents/4040
劉艷偉 (2019)。使用IBMCloud-Annotations訓練影像辨識模型(結合iOS和Android的影像辨識APP)。Medium。
https://yanwei-liu.medium.com/%E4%BD%BF%E7%94%A8ibm-cloud-annotations%E8%A8%93%E7%B7%B4%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98%E6%A8%A1%E5%9E%8B-%E7%B5%90%E5%90%88ios%E5%92%8Candroid%E7%9A%84%E5%BD%B1%E5%83%8F%E8%BE%A8%E8%AD%98app-24cd49676b9e
張郎屋 (2020)。用IBM Cloud anntations來標註數據資料。Blog。
https://tn00343140a.pixnet.net/blog/post/325744242-%e7%94%a8ibm-cloud-annotations%e4%be%86%e6%a8%99%e8%a8%bb%e6%95%b8%e6%93%9a%e8%b3%87%e6%96%99
Wei (2022)。Python 第一課:Google Colaboratory 介紹。Youtube。
https://www.youtube.com/watch?v=eJCXFIoOwdw
張家銘 (2022)。YOLOv7 介紹。財團法人台灣人工智慧學校基金會。
https://aiacademy.tw/yolov7/
李謦伊 (2022)。YOLOv7 論文閱讀。Medium。
https://medium.com/ching-i/yolo%E6%BC%94%E9%80%B2-yolov7-%E8%AB%96%E6%96%87%E9%96%B1%E8%AE%80-97b0e914bdbe
台灣物種名錄資料庫 (2023)。海洋生物。
https://taibnet.sinica.edu.tw/
農業科技決策資訊平台 (2023)。水產領域。
https://agritech-foresight.atri.org.tw/article/list/1359
邵廣昭 (2024)。台灣魚類資料庫。中央研究院數位文化中心&中央研究院生物多樣性研究中心。
https://fishdb.sinica.edu.tw/chi/home.php
氣象資料開放平台 (2024)。資料主題。中央氣象署。
https://opendata.cwa.gov.tw/index
海域遊憩平台 (2024)。海域資訊。海洋委員會。
https://ocean.taiwan.gov.tw/
海洋委員會海洋保育署 (2024)。永續資源。
https://www.oca.gov.tw/ch/index.jsp
海洋保育網 (2024)。海洋委員會海洋保育署。
https://iocean.oca.gov.tw/OCA_OceanConservation/Default.aspx
海大數位典藏 (2024)。海洋大學海洋生物研究所典藏魚類標本總覽。國立台灣海洋大學海洋生物研究所。
https://digifish.biodiv.tw/index.php

英文文獻
Siddiqui, S. A., Salman, A., Malik, M. I., Shafait, F., Mian, A., Shortis, M. R., Harvey, E. S. ( 2018 ). Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES Journal of Marine Science, 75(1), 374–389.
http://dx.doi.org/10.1093/icesjms/fsx109
Li, X. ( 2015 ). Fish recognition in underwater videos using a combined texture and color feature descriptor. Journal of Ocean University of China, 14(5), 811-818.
http://dx.doi.org/10.1007/s11802-015-2654-2
Redmon, J., & Farhadi, A. ( 2016 ). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR2017 (pp. 6517-6525).
https://doi.org/10.48550/arXiv.1612.08242
Redmon,J., Divvala,S., Girshick,R.,& Farhadi,A. ( 2016 ).You Only Look Once: Unified, Real-Time Object Detection.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016 -Decem,779-788.
https://doi.org/10.48550/arXiv.1506.02640
Rathi, D., Jain, S., & Indu, S. ( 2017 ).Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning.In 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) (pp. 1-6). Bangalore, India.
http://dx.doi.org/10.1109/ICAPR.2017.8593044
Murugaiyan, J., Palaniappan, M., Durairaj, T., & Muthukumar, V. ( 2021 ). Fish species recognition using transfer learning techniques.International Journal of Advances in Intelligent Informatics, 7(2), 188-197. https://doi.org/10.26555/ijain.v7i2.610
Qin, H., Li, X., Liang, J., Peng, Y., & Zhang, C. ( 2016 ). DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing, 187, 49-58.
http://dx.doi.org/10.1016/j.neucom.2015.10.122
Redmon, J., & Farhadi, A. ( 2018 ). YOLOv3: An Incremental Improvement https://arxiv.org/abs/1804.02767
Deep, B. V., & Dash, R. ( 2019 ). Underwater Fish Species Recognition Using Deep Learning Techniques. .In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 665-669). Noida, India.
https://doi.org/10.1109/SPIN.2019.8711657
Zheng, Z., et al. ( 2018 ). Fish Recognition from a Vessel Camera Using Deep Convolutional Neural Network and Data Augmentation. In 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) (pp. 1-5). Kobe, Japan.
https://doi.org/10.1109/OCEANSKOBE.2018.8559314.
Tan, M., & Le, Q. V.( 2019 ).EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
https://doi.org/10.48550/arXiv.1905.11946
Elhabyan, A. ( 2019 ).Underwater Fish Recognition Using Convolutional Neural Networks and Data Augmentation Techniques. In 2019 International Conference on Computer and Applications (ICCA) (pp. 1-5) . IEEE.
https://doi.org/10.1109/COMAPP.2019.8869581
Konovalov, D. A., Saleh, A., Bradley, M., Sankupellay, M., Marini, S., & Sheaves, M. ( 2019 ). Underwater Fish Detection with Weak Multi-Domain Supervision. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Budapest, Hungary.
https://doi.org/10.1109/IJCNN.2019.8851907
Bhuiyan, M. A. S. ( 2019 ) Fish Recognition Using Convolutional Neural Network and Fish Dataset. In 2019 IEEE International Conference on Electro/Information Technology (EIT) (pp. 548-553).
https://doi.org/10.1109/EIT.2019.8833364
Xu, W., & Matzner, S. ( 2018 ) . Underwater Fish Detection using Deep Learning for Water Power Applications.
https://doi.org/10.48550/arXiv.1811.01494
Ghafoor, K. ( 2019 ). Real-Time Fish Detection and Tracking in Underwater Video Streams Using YOLO. In Proceedings of the 2019 2nd International Conference on Data Science and Information Technology (pp. 152-157). IEEE.
https://doi.org/10.1109/DSIT47788.2019.8983035
Varalakshmi, P., & Julanta Leela Rachel, J. ( 2019 ). Recognition Of Fish Categories Using Deep Learning Technique. In 2019 3rd International Conference on Computing and Communications Technologies (ICCCT) (pp. 168-172). Chennai, India.
https://doi.org/10.1109/ICCCT2.2019.8824916.
Moulahi, B. ( 2020 ). Underwater fish detection and recognition based on YOLOv3. Journal of Ambient Intelligence and Humanized Computing, 11(1), 475-484.
https://doi.org/10.1007/s12652-019-01513-y
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. ( 2020 ). Scaled-YOLOv4: Scaling Cross Stage Partial Network.
https://doi.org/10.48550/arXiv.2011.08036
Knausgård, K. M., Wiklund, A., Sørdalen, T. K., Halvorsen, K., Kleiven, A. R., Jiao, L., & Goodwin, M. ( 2020 ). Temperate Fish Detection and Classification: A Deep Learning based Approach.
https://doi.org/10.48550/arXiv.2005.07518
Cui, S., Zhou, Y., Wang, Y., & Zhai, L. ( 2020 ). Fish Detection Using Deep Learning. Applied Computational Intelligence and Soft Computing.
https://doi.org/10.1155/2020/3738108
Raza, K., & Hong, S. ( 2020 ).Fast and Accurate Fish Detection Design with Improved YOLO-v3 Model and Transfer Learning .International Journal of Advanced Computer Science and Applications(IJACSA)
https://dx.doi.org/10.14569/IJACSA.2020.0110202
Cai, K., Miao, X., Wang, W., Pang, H., Liu, Y., & Song, J. ( 2020 ). A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone. Aquacultural Engineering, 91, 102117.
https://doi.org/10.1016/j.aquaeng.2020.102117
Mathisen, B. M., Bach, K., Meidell, E., Måløy, H., & Sjøblom, E. S. ( 2020 ). FishNet: A Unified Embedding for Salmon Recognition.
https://doi.org/10.48550/arXiv.2010.10475
Alshdaifat, N. F. F., Talib, A. Z., & Osman, M. A. ( 2020 ). Improved deep learning framework for fish segmentation in underwater videos. Ecological Informatics, 59,101121.
https://doi.org/10.1016/j.ecoinf.2020.101121.
Zhang, T., Yang, Y., Liu, Y., Liu, C., Zhao, R., Li, D., & Shi, C. ( 2024 ). Fully automatic system for fish biomass estimation based on deep neural network. Ecological Informatics, 79, 102399.
https://doi.org/10.1016/j.ecoinf.2023.102399.
Tarling, P., Cantor, M., Clapés, A., & Escalera, S ( 2022 ) .Deep learning with self-supervision and uncertainty regularization to count fish in underwater images. PLOS ONE.
https://doi.org/10.1371/journal.pone.0267759
Nair, S. R. ( 2021 ) .A Deep Learning Framework for Underwater Fish Detection and Classification. IEEE Journal of Oceanic Engineering, 46(3), 956-966.
https://doi.org/10.1109/JOE.2020.3013537
Fernandez Garcia, G., Martignac, F., Nevoux, M., Beaulaton, L., & Corpetti, T. ( 2021 ). A deep neural network for multi-species fish detection using multiple acoustic cameras.arXiv preprint arXiv:2109.10664.
https://arxiv.org/abs/2109.10664
Ben Ali, N. ( 2021 ) . Deep Transfer Learning for Underwater Fish Species Identification and Recognition Using Shallow Water Images. IEEE Access, 9, 78221-78235.
https://doi.org/10.1109/ACCESS.2021.3089472
Saleh, A., Sheaves, M., & Rahimi Azghadi, M. ( 2021 ) .Computer vision and deep learning for fish classification in underwater habitats: A survey. Fisheries Management and Ecology, 28(2), 121-135.
https://doi.org/10.1111/faf.12666
Raza, K., & Hong, S. ( 2020 ). Fast and Accurate Fish Detection Design with Improved YOLO-v3 Model and Transfer Learning. International Journal of Advanced Computer Science and Applications, 11.
https://doi.org/10.14569/ijacsa.2020.0110202
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. ( 2021 ) . RepVGG: Making VGG-style ConvNets Great Again. arXiv preprint arXiv:2101.03697.
https://arxiv.org/abs/2101.03697
Rezaei, M. ( 2021 ).A review of fish detection and recognition using deep learning in underwater environments. Journal of Ocean Engineering and Science, 6(2), 230-242.
https://doi.org/10.1016/j.joes.2021.03.006
Cheng, L., & He, C. ( 2021 ). Fish Recognition Based on Deep Residual Shrinkage Network. In 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE) (pp. 36-39). Wuhan, China.
https://doi.org/10.1109/RCAE53607.2021.9638791.
Knausgård, K. M., Wiklund, A., Sørdalen, T. K., et al. ( 2022 ). Temperate fish detection and classification: a deep learning based approach. Applied Intelligence, 52(22), 6988–7001.
https://doi.org/10.1007/s10489-020-02154-9
Alaba, S. Y., Nabi, M. M., Shah, C., Prior, J., Campbell, M. D., Wallace, F., Ball, J. E., & Moorhead, R. ( 2022 ). Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset. Sensors, 22(21), 8268.
https://doi.org/10.3390/s22218268
N. N, A. Siva Kumaran K, A. A, A. V. S, & B. M. J. ( 2022 ). Convolutional Neural Networks (CNN) based Marine Species Identification. In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 602-607). Pudukkottai, India.
https://doi.org/10.1109/ICACRS55517.2022.10029109.
Vishnu, K., Matt, R., Frank, S., Jean, Q., Luis, T., & Chris, W. ( 2022 ) .Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning . Frontiers in Marine Science.
https://doi.org/10.3389/fmars.2021.823173
Liu, L., & Yu, W. ( 2022 ) . Underwater Image Saliency Detection Via Attention-based Mechanism. Journal of Physics: Conference Series, 2189(1), 012012.
https://doi.org/10.1088/1742-6596/2189/1/012012
Garcia-d’Urso, N., Galan-Cuenca, A., Pérez-Sánchez, P., Climent-Pérez, P., Fuster-Guillo, A., Azorin-Lopez, J., Saval-Calvo, M., Guillén-Nieto, J. E., & Soler-Capdepón, G. ( 2022 ) . The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation. Scientific Data, https://doi.org/10.1038/s41597-022-01416-0
Tamou, A., Benzinou, A., & Nasreddine, K. ( 2022 ) . Targeted Data Augmentation and Hierarchical Classification with Deep Learning for Fish Species Identification in Underwater Images. Journal of Imaging, 8(8), 214.
https://doi.org/10.3390/jimaging8080214
Abinaya, N. S., Susan, D., & Sidharthan, R. K. ( 2022 ) .Deep learning-based segmental analysis of fish for biomass estimation in an occulted environment. Computers and Electronics in Agriculture, 199, 106985.
https://doi.org/10.1016/j.compag.2022.106985
Pagire, V., & Phadke, A. ( 2022 ) .Underwater Fish Detection and Classification using Deep Learning. 2022 International Conference on Innovative Computing and Communication and Security (ICICCSP). (pp. 1-4). Hyderabad, India.
https://doi.org/10.1109/ICICCSP53532.2022.9862410
Saleh, A., Sheaves, M., & Rahimi Azghadi, M. ( 2022 ) . Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey.Fisheries and Aquatic Sciences.
https://doi.org/10.1111/faf.12666
Islam, M., Ani, J. F., Rahman, A., & Zaman, Z. ( 2021 ) . Fake Hilsa Fish Detection Using Machine Vision. In Islam, M., Zhang, Y., Roy, N., & Alam, M. (Eds.), Advances in Computer Science and Information Engineering. Springer.
https://doi.org/10.1007/978-981-16-0586-4_14
Yang, K., Yau, J., Fei-Fei, L., Deng, J., & Russakovsky, O. ( 2021 ) ImageNet.
https://image-net.org/index.php
Froese, R., & Pauly, D. (Eds.). ( 2023 ). FishBase. World Wide Web electronic publication. https://fishbase.mnhn.fr/search.php
Lampa, M. D., Librojo, R. C., & Calamba, M. M. ( 2022 ). Fish Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4323384
Ulucan, O., Karakaya, D., & Turkan, M. ( 2020 ). A Large-Scale Dataset for Fish Segmentation and Classification. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE
https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset?resource=download
Dwyer, B. ( 2022 ).How to Train YOLOv7 on a Custom Dataset, roboflow.
https://colab.research.google.com/drive/1X9A8odmK4k6l26NDviiT6dd6TgR-piOa#scrollTo=6AGhNOSSHY4_

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