博碩士論文 104552002 詳細資訊




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姓名 何亮皞(Liang-Hao Ho)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 牛臉斑痕雙模態乳牛身份識別
(Dairy Cow Identity Recognition System Based on Spot Contour Features)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-9-10以後開放)
摘要(中) 國內現階段酪農業所面對的最大困境為勞動力不足,首當其衝為牛隻管理上的問題。目前推動之智慧化農場管理旨在以偵測技術進行精準管理,目前的生物識別多以虹膜、口套、耳標等作為辨識目標,惟上述目標皆需極高的影像品質,在實務上難以達到,加上維護成本,故本研究擬以牛隻原有的斑痕與臉部特徵作為辨識目標。

本研究先以25 隻牛做為實驗樣本,共計121 張影像進行訓練與測試。先以YOLOv4
偵測,擷取臉部與斑痕特徵影像並分割,通過Triplet 三元組神經網路取得影像特徵向量,計算樣本間的歐氏距離,進行相似度比對,最後得到牛隻身份識別結果。實驗結果可發現使用單一特徵作為識別條件,牛臉辨識率為92%,斑痕辨識率為88%。最後通過混合神經網路的方式,設計了一個雙模態神經網路,經由PNN 機率神經網路算出牛隻樣本的機率進行身份識別,辨識率可達96%。因此證明在少樣本實驗情境下,透過雙模態神經網路可以有效地提升生物識別效能。
摘要(英) The biggest dilemma faced by dairy farming in Taiwan currently is the lack of labor, especially the problem of cattle management. The current promotion of intelligent farm management aims at accurate management with detection technology. At present, biometric identification mostly uses iris, muzzle, ear tag, etc. as identification targets. However, the above targets all require extremely high image quality, which is difficult in practice. In addition to the maintenance cost, this study intends to use the original scars and facial features of cattle as the identification target.

In this study, 25 cows were used as experimental samples, and a total of 121 images wereused for training and testing. First, YOLOv4 is used to detect, capture and segment feature images of faces and scars, obtain image feature vectors through Triplet network, calculate the Euclidean distance between samples, compare the similarity, and finally get the cattle identification result. The experimental results show that using a single feature as the recognition condition, the recognition rate of cow face is 92%, and the recognition rate of spots is 88%.
Finally, through the method of hybrid neural network, a dual-modal hybrid neural network is designed, and the probability of cattle samples is calculated through the PNN probability neural network for identification, and the recognition rate can reach 96%. Therefore, it is proved that the dual-modal hybrid neural network can effectively improve the biometric identification performance under the few-sample experimental situation.
關鍵字(中) ★ 雙模態
★ 深度學習
★ 身份識別
關鍵字(英)
論文目次 摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 4
1.3 論文架構 4
第二章、文獻回顧 5
2.1 影像分割 5
2.2 物件偵測 5
2.1.1 YOLO 物件偵測 6
2.1.2 YOLO 模型評估 9
2.3 深度學習分類器 10
2.3.1 Siamese Neural Network 11
2.3.2 Loss Function 12
2.4 EfficientNet 14
第三章、乳牛身份識別系統設計 17
3.1 MIAT 系統設計方法論 17
3.2 乳牛身份識別系統架構 19
3.3 牛隻偵測分割模組 23
3.4 Triplet 神經網路訓練模組 25
3.5 牛隻身份識別模組 27
第四章、實驗 31
4.1 實驗環境 31
4.2 影像切割實驗 33
4.2.1 以Label 建立Label 33
4.2.2 YOLOv4 模型訓練 34
4.2.3 乳牛分割實驗 36
4.3 乳牛身份識別實驗 37
4.3.1 三元組神經網路訓練 38
4.3.2 特徵融合識別 47
第五章、結論與未來展望 49
5.1 結論 49
5.2 未來展望 49
參考文獻 50
參考文獻 [1]. 陳昱宏,臺灣中小型酪農業的經營挑戰與因應策略,中興大學碩士論文,2020。
[2]. 施石軒,卓越雜誌(https://www.cmoney.tw/notes/note-detail.aspx?nid=57200),2016。
[3]. Thi Thi Z., Mamber. Image Technology based cow identification system using deep learning.2018
[4]. Lu, Y., He, X., Wen, Y. and Wang, P.S., “A new cow identification system based on iris analysis and recognition”, In International Journal of Biometrics, vol.6, no1, pp.18-32, 2014.
[5]. Awad, A. I., Zawbaa, H.M., Mahmoud, H.A., Nabi, E.H.H.A., Fayed, R.H. and Hassanien, A.E., “A robust cow identification scheme using muzzle print images”, In 2013 Federated IEEE Conference on Computer Science and Information Systems (FedCSIS), pp.529-534, September, 2013.”
[6]. Ilestrand, M., “Automatic eartag recognition on dairy cows in real barn environment”, 2017.
[7]. W. Andrew, C. Greatwood, and T. Burghardt, "Visual localisation and individual identification of holstein friesian cattle via deep learning," in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017, pp. 2850-2859.
[8]. Hoffer, Elad, and Nir Ailon. "Deep metric learning using triplet network." International workshop on similarity-based pattern recognition. Springer, Cham, 2015.
[9]. X. Dong, J. Shen, D. Wu, K. Guo, X. Jin and F. Porikli, "Quadruplet network with one-shot learning for fast visual object tracking," in IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3516-3527, July 2019.
[10]. 180年農機老店,要用AI跟雜草開戰。2022年9月9日,取自https://ai-blog.flow.tw/ai-in-agriculture。
[11]. Linda G. Shapiro and George C. Stockman (2001):「Computer Vision」, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3
[12]. A. Bochkovskiy, C. Wang, H. Mark Liao, "YOLOv4: optimal speed and accuracy of object detection," arXiv preprint arXiv:.10934, 2004
[13]. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M. J. a. p. a. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection.
[14]. YOLO—You Only Look Once 介紹。2022年9月9日,取自https://medium.com/@c.c.lo/yolo-%E4%BB%8B%E7%B4%B9-4307e79524fe。
[15]. 目標檢測網路之YOLOv2。2022年9月9日,取自http://www.mamicode.com/info-detail-2232517.html。
[16]. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517-6525.
[17]. P. Garg, D. R. Chowdhury and V. N. More, "Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD,"(2019) 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019, pp. 1-5, doi: 10.1109/ICCCNT45670.2019.8944491.
[18]. J. Redmon and A. Farhadi, "YOLOv3: an incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
[19]. Yolo:基於深度學習的物件偵測 (含YoloV3) 。2022年9月9日,取自https://mropengate.blogspot.com/2018/06/yolo-yolov3.html。
[20]. Huang, C.-Y.; Lin, I.-C.; Liu, Y.-L. Applying Deep Learning to Construct a Defect Detection System for Ceramic Substrates. Appl. Sci. 2022, 12, 2269. https://doi.org/10.3390/app12052269
[21]. Z. Zhang and V. Saligrama. Zero-shot learning via joint semantic similarity embedding. In CVPR, 2016.
[22]. Garcia, V., and Bruna, J. Few-shot learning with graph neural networks. Proceedings of the International Conference on Learning Representations (2018).
[23]. 施筱萱(2014)。透過圖像分割使用多個方法對人臉進行邊緣檢測。國立交通大學碩士論文。
[24]. 180年農機老店,要用 AI 跟雜草開戰。2022年9月9日,取自https://ai-blog.flow.tw/ai-in-agriculture
[25]. Awad, A. I., Zawbaa, H.M., Mahmoud, H.A., Nabi, E.H.H.A., Fayed, R.H. and Hassanien, A.E., “A robust cow identification scheme using muzzle print images”, In 2013 Federated IEEE Conference on Computer Science and Information Systems (FedCSIS), pp.529-534, September, 2013.”
[26]. 深度學習入門教程:常見的損失函數大全。2022年9月9日,取自https://kknews.cc/code/9opor8j.html。
[27]. 人臉辨識模型Google Facenet介紹與使用。2022年9月9日,取自https://chtseng.wordpress.com/2018/12/09/%E7%95%B6%E7%B4%85%E7%9A%84%E4%BA%BA%E8%87%89%E8%BE%A8%E8%AD%98%E6%A8%A1%E5%9E%8B-google-facenet-%E4%BB%8B%E7%B4%B9%E8%88%87%E4%BD%BF%E7%94%A8/。
[28]. Triplet Loss原理與應用。2022年9月9日,取自https://www.gushiciku.cn/pl/p7KY/zh-hk
[29]. Mingxing Tan & Quoc V. Le EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, 2019
指導教授 陳慶瀚 審核日期 2022-9-14
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