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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃乾綱(Chien-Kang Huang) | |
dc.contributor.author | Chen-Ting Liao | en |
dc.contributor.author | 廖宸廷 | zh_TW |
dc.date.accessioned | 2021-06-17T02:13:46Z | - |
dc.date.available | 2020-08-24 | |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-17 | |
dc.identifier.citation | 1. 陳中興, 中華民國行政院農委會 農政與農情 205期, 民98年7月 2. Z. Qu, Mengmeng Yu and Junxue Liu, 'Real-time traffic vehicle tracking based on improved MoG background extraction and motion segmentation,' 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics, Harbin, 2010 3. R. Girshick, Fast R-CNN. arXiv:1504.08083, 2015 4. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. arXiv preprint arXiv:1506.02640, 2015 5. Mingxing Tan, Ruoming Pang, and Quoc V Le. EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020 6. S. Xie and Z. Tu. Holistically-nested edge detection. In ICCV, 2015 7. Y. Liu, M.-M. Cheng, X. Hu, K. Wang, and X. Bai. Richer convolutional features for edge detection. In IEEE Conference on Computer Vision and Pattern Recognition, 2017 8. T. Liu, G. Teng, and W. Fu, Research and development of pig weight estimation system based on image, 2011 Int. Conf. Electron. Commun. Control, pp. 2774–2777, 2011 9. P. Buayai, K. Piewthongngam, C. K. Leung, and K. Runapongsa Saikaew, Semi-Automatic Pig Weight Estimation Using Digital Image Analysis, Applied Engineering in Agriculture, 2019 10. Smisek J., Jancosek M., Pajdla T. (2013) 3D with Kinect. In: Fossati A., Gall J., Grabner H., Ren X., Konolige K. (eds) Consumer Depth Cameras for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4640-7_1 11. Leonid Keselman, John Iselin Woodfill, Anders Grunnet-Jepsen, Achintya Bhowmik; Intel RealSense Stereoscopic Depth Cameras, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 1-10 12. Shi, C.; Teng, G.; Li, Z. An Approach of Pig Weight Estimation using Binocular Stereo System based on LabVIEW. Comput. Electron. Agric. 2016, 129, 37–43 13. Pezzuolo, A, Guarino, M, Sartori, L, González, L.A, Marinello, F, On-barn pig weight estimation based on body measurement by means of a Kinect v1 sensor. Comput. Electron. Agric. 2018 14. Jun, K.; Kim, S.; Ji, H. Estimating Pig Weights from Images without Constraint on Posture and Illumination. Comput. Electron. Agric. 2018, 153, 169–176 15. J. Kongsro, “Estimation of pig weight using a Microsoft Kinect prototype imaging system,” Computers and Electronics in Agriculture, vol. 109, pp. 32-35, 2014 16. Google AI, Open Image V5, https://storage.googleapis.com/openimages/web/download.html 17. Yun Liu and Ming-Ming Cheng, Xiaowei Hu and K Wang and X Bai, Richer Convolutional Features for Edge Detection, IEEE CVPR 2017 https://github.com/meteorshowers/RCF-pytorch 18. Schofield, C. P. Evaluation of image analysis as a means of estimating the weight of pigs. Journal of Agricultural Engineering Research, 1990 19. Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020 20. Psota, E., Mittek, M., Pérez, L., Schmidt, T., Mote, B. (2019). Multi-Pig Part Detection and Association with a Fully-Convolutional Network. Sensors, 2019 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68161 | - |
dc.description.abstract | 豬隻體型量測是豬隻養殖戶重要的工作之一,在育種、檢視飼料效率、豬隻健康控管上皆扮演重要角色,其中體重也是決定豬隻是否達到出貨標準的條件。目前豬隻體型量測以人工為主,逐一量測體型是十分耗費體力的工作,豬隻在受到控制的時候會用力的掙脫,過程中常常導致量測人員受傷,也容易使豬隻產生壓力,對其健康造成不良的影響。 本研究提出一套非接觸式的豬隻體重量測系統,使用單一RealSense深度攝影機拍攝欄位中豬隻的彩色影像與深度影像,藉由Yolo物件辨識模型在彩色影像中偵測出豬隻所在位置,再使用RCF邊緣偵測模型擷取出範圍內豬隻身體的封閉輪廓。使用深度影像所提供的距離資訊,以針孔成像原理將輪廓內面積還原成現實尺度的可視面積,以影像實現簡單又有效率的豬隻體重量測。 本系統減少需要人為決定的參數,使系統在不同光源及環境下都可以正常使用,豬隻不同的位置與站立方式之下也能進行可視面積的修正,增加系統的強健性。在研究中我們對同樣的豬隻進行前後兩次的體型量測,觀察到投影面積及立體模型的表面積在豬隻生長過程中有顯著的成長,豬隻體重也與投影面積有一定的關係。 | zh_TW |
dc.description.abstract | Measuring pig size is one of the import tasks for the farmers. It plays an important role in breeding, check feed efficiency and health control. Among all the body features, weight is used to determine whether a pig is reaching the shipping standard. These days, pig body measurement is mainly manual. It is a labor-intensive job. When a pig is under control, it will struggle to escape, which often causes farmers to be injured. It also put stress on pigs, which has a negative impact on pig’s health. This research proposes a non-contact pig weight measurement system that uses a RealSense depth camera to capture color images and depth images. We use Yolo object detection model to find the location of the pig in color image, and use RCF edge detection model to extract the close contour of the pig. We use pinhole imaging principle to calculate the realistic scale area of the contour by the depth information from depth image. A simple and efficient pig body weight measurement can be achieved with images. The system reduces the parameters that need to be determined manually, so that the system can be used normally under different environments, and the pig contour area can be corrected under different positions and postures of the pigs, which increases the robustness of the system. In the study, we took two measurements on the same pigs before and after, observed the contour area of pigs and the surface area of the pigs model. We found that contour area and surface area had significant growth during the growth of the pigs, and the weight of the pigs also had a certain relationship with the projected area. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:13:46Z (GMT). No. of bitstreams: 1 U0001-1708202016090500.pdf: 4779888 bytes, checksum: a95f138f58c071f39a1b67511be18360 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 誌謝 b 中文摘要 c ABSTRACT d 目錄 1 圖目錄 4 Chapter1 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目標 2 1.4 研究貢獻 3 Chapter2 文獻探討 4 2.1 相關研究 4 2.2 豬隻辨識 5 2.2.1 背景減除(Background Subtraction) 5 2.2.2 物件辨識(Objection Detection) 6 2.3 獲得豬隻邊緣輪廓 8 2.3.1 影像二值化(Binarization) 8 2.3.2 邊緣偵測(Edge Detection) 9 2.4 計算豬隻實際尺寸 15 2.4.1 針孔成像 15 2.4.2 深度攝影機 16 2.5 推估豬隻影像與體重關係 17 Chapter3 研究方法 20 3.1 影像拍攝 21 3.2 物件辨識 23 3.2.1 物件辨識模型 23 3.2.2 模型訓練 24 3.3 邊緣偵測 26 3.3.1 邊緣偵測模型 26 3.3.2 輪廓篩選 27 3.4 計算實際尺寸 29 3.4.1 彩色影像與深度影像對齊 29 3.4.2 計算豬隻距離 29 3.4.3 實際投影面積計算 31 3.4.4 投影面積修正 32 3.4.5 3D點雲模型量測面積 34 Chapter4 實驗結果 38 4.1 實驗影像與設備 38 4.2 實驗豬隻體型資訊 41 4.3 豬隻投影面積 42 4.4 立體模型表面積 46 4.5 影像量測與實際體重比較 47 Chapter5 總結與未來展望 48 5.1 結論 48 5.2 未來展望 52 參考文獻 54 | |
dc.language.iso | zh-TW | |
dc.title | 利用深度攝影機進行自動化的豬隻體重量測 | zh_TW |
dc.title | Automatically Measuring Pig Weights by Using Depth Camera | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林恩仲(En-Chung Lin),張恆華(Herng-Hua Chang),丁肇隆(Chao-Lung Ting) | |
dc.subject.keyword | 深度攝影機,深度學習,物件辨識,豬, | zh_TW |
dc.subject.keyword | Depth Camera,Deep Learning,Object Detection,Pig, | en |
dc.relation.page | 56 | |
dc.identifier.doi | 10.6342/NTU202003788 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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