跳到主要內容

臺灣博碩士論文加值系統

(3.236.110.106) 您好!臺灣時間:2021/07/29 16:36
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:葉期財
研究生(外文):Yeh, Chi-Tsai
論文名稱:整合物聯網與雲端計算的智慧數蝦應用
論文名稱(外文):Integration of IoT and Cloud Computing for Intelligent Shrimp Counting
指導教授:陳銘志陳銘志引用關係
指導教授(外文):Chen, Ming-Chih
口試委員:蕭勝夫張保榮莊作彬梁財春吳毓恩江傳文
口試委員(外文):Hsiao, Shen-FuChang, Bao RongJuang, Tso-BingLiang, Tsair-ChunWu, Yu-EnChiang, Chuan-Wen
口試日期:2019-10-16
學位類別:博士
校院名稱:國立高雄科技大學
系所名稱:工學院工程科技博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:108
語文別:中文
論文頁數:100
中文關鍵詞:電腦視覺機器學習雲端計算物聯網
外文關鍵詞:computer visionmachine learningcloud computinginternet of thing
相關次數:
  • 被引用被引用:0
  • 點閱點閱:322
  • 評分評分:
  • 下載下載:79
  • 收藏至我的研究室書目清單書目收藏:2
本研究應用電腦視覺技術OpenCV (Open Source Computer Vision),並結合機器學習中的集群分析K-means clustering,提出一個可以快速計算極火蝦數量的智慧數蝦系統。針對企業需求,本系統不僅要考慮執行效率與準確度,同時還需注意系統成本與彈性,基於上述的考量將計算蝦隻的影像處理實作於AWS (Amazon Web Service) Lambda,並透過REST(REpresentational State Transfer) Web Service的呼叫方式來存取,這樣的架構可以在請求數量暴增或是影像處理優化的情況下,均可無縫的進行更新,且把主要計算置於雲端就可以按照使用量來計費,減少終端裝置樹莓派的成本。本研究在數蝦影像處理的流程依序是:1. 取得極火蝦影像的HSV(Hue Saturation Value)色彩空間特徵值;2. 根據該影像特徵值過濾目標影像;3. 進行二值化;4. 影像分割找出輪廓,根據閥值找出極火蝦的候選區域,計算極火蝦的候選區域的面積;5. 透過分群決定蝦隻面積;6. 最後計算蝦隻數量。透過實驗結果顯示在計算150隻以下的極火蝦,影像處理的部分可以在0.1秒內計算完畢,且準確率高達95%以上。
This research adopts computer vision technology, OpenCV (Open Source Computer Vision), combined with K-means in machine learning to propose an intelligent shrimp counting system that can quickly calculate the number of the shrimps. In the case of considering the higher flexibility and lower cost of this application, we introduce the emerging technologies of serverless and IoT (Internet of Thing) such as AWS (Amazon Web Service) Lambda and Raspberry Pi. This research is, therefore, mainly designed to apply the computer vision to undertake the counting of shrimps automatically. OpenCV provides plenty of computer vision applications and often cooperates with the Raspberry Pi and AWS Lambda. The steps of image processing for accurately counting the shrimps are as follows: (1) capture the image, (2) filter and remain the sampling color, (3) threshold the filtered image, (4) contour the blobs in the image (5) determine the area of one shrimp (6) count the number of the shrimps. Concerning the performance and flexibility, we embed the image process into AWS Lambda function. Experimental results of counting shrimps (Neocaridina heteropoda var. red) show that our proposed application completes the counting operation of 150 shrimps in 0.1 second and the accuracy is up to 95%.
摘 要 I
ABSTRACT II
目 錄 IV
圖目錄 VIII
表目錄 X
一、緒論 1
1.1 動機與背景 1
1.2 研究目的 2
1.3 研究方法 3
1.4 研究範圍與限制 4
1.5 研究架構及流程 4
二、文獻探討 6
2.1 電腦視覺與水產計數上的應用 6
2.1.1 電腦視覺與色彩空間 6
2.1.2 水產養殖在自動計數的電腦視覺文獻 9
2.2 電腦視覺應用技術 15
2.2.1 計算閥值(thresholding) 16
2.2.2 K-means集群 (K-means Clustering) 17
2.2.3 OpenCV應用工具 19
2.2.4 Python 21
2.3 物聯網 23
2.3.1 物聯網的定義 23
2.3.2 樹莓派 27
2.4 雲端計算與服務 28
2.4.1 雲端計算 28
2.4.2 亞馬遜雲端服務 35
三、系統實作 48
3.1 定義問題 48
3.2 系統架構 49
3.3 系統實作遭遇的問題 52
3.3.1 背景刪除 53
3.3.2 如何判斷一隻蝦 54
3.3.3 計數誤差分析 56
3.4 系統流程與軟體需求 57
3.4.1 系統流程 57
3.4.2 系統軟體需求 59
四、實驗結果 64
4.1 使用者介面 64
4.1.1 行動裝置APP 64
4.1.2 即時監控系統 65
4.2 元件間溝通介面 67
4.3 實驗成本 73
4.4 與先前研究結果比較 75
五、結論與未來展望 77
5.1 結論 77
5.2 未來展望 78
參考文獻 79


[1] Alver, M. O., Tennøy, T., Alfredsen, J. A., & Øie, G. (2007). Automatic measurement of rotifer Brachionus plicatilis densities in first feeding tanks. Aquacultural engineering, 36(2), 115-121.
[2] Barbedo, J. G. A. (2012, June). Method for counting microorganisms and colonies in microscopic images. In 2012 12th International Conference on Computational Science and Its Applications (pp. 83-87). IEEE.
[3] Barbedo, J. G. A. (2014). Using digital image processing for counting whiteflies on soybean leaves. Journal of Asia-Pacific Entomology, 17(4), 685-694.
[4] Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of food engineering, 61(1), 3-16.
[5] Canalys. (2019, February 4). Cloud market share Q4 2018 and full year 2018. Retrieved from https://www.canalys.com/newsroom/cloud-market-share-q4-2018-and-full-year-2018.
[6] Coronel, L., Badoy, W., & Namoco, C. (2018). Identification of an efficient filtering-segmentation technique for automated counting of fish fingerlings. Int. Arab J. Inf. Technol., 15(4), 708-714..
[7] Das, B. K., Jha, K. K., & Dutta, H. S. (2014, March). A new approach for segmentation and identification of disease affected blood cells. In 2014 International Conference on Intelligent Computing Applications (pp. 208-212). IEEE.
[8] Flores, A., Crisostomo, P., & Lopez, J. (2008, April). Peruvian scallop larvae counting system using image processing techniques. In 2008 7th International Caribbean Conference on Devices, Circuits and Systems (pp. 1-4). IEEE.
[9] Friedland, K. D., Ama-Abasi, D., Manning, M., Clarke, L., Kligys, G., & Chambers, R. C. (2005). Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. Journal of Sea Research, 54(4), 307-316.
[10] Friedland, K. D., Ama-Abasi, D., Manning, M., Clarke, L., Kligys, G., & Chambers, R. C. (2005). Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. Journal of Sea Research, 54(4), 307-316.
[11] Fuad, M. A. M., Ab Ghani, M. R., Ghazali, R., Sulaima, M. F., Jali, M. H., Sutikno, T., & Jano, Z. (2017). A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique. Telkomnika, 15(3), 1199-1206.
[12] Janakiram MSV. (2019, Mar 3). 10 Key Takeaways From RightScale 2019 State Of The Cloud Report From Flexera. Retrieved from https://www.forbes.com/sites/janakirammsv/2019/03/03/10-key-takeaways-from-rightscale-2019-state-of-the-cloud-report-from-flexera/#2362e0941396.
[13] Khantuwan, W., & Khiripet, N. (2012, May). Live shrimp larvae counting method using co-occurrence color histogram. In 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (pp. 1-4). IEEE.
[14] Li, A., Yang, X., Kandula, S., & Zhang, M. (2010, November). CloudCmp: comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (pp. 1-14). ACM.
[15] Li, A., Yang, X., Kandula, S., & Zhang, M. (2011). Comparing public-cloud providers. IEEE Internet Computing, 15(2), 50-53.
[16] Lu, Y., & Qin, S. (2010, June). Stored-grain insect image processing based on a Hidden Markov Model. In 2010 International Conference on Electrical and Control Engineering (pp. 1997-2000). IEEE.
[17] Martin, A., Sathish, D., Balachander, C., Hariprasath, T., & Krishnamoorthi, G. (2015, February). Identification and counting of pests using extended region grow algorithm. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1229-1234). IEEE.
[18] Mohamed, M., & Far, B. (2012, October). An enhanced threshold based technique for white blood cells nuclei automatic segmentation. In 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 202-207). IEEE.
[19] Newbury, P. F., Culverhouse, P. F., & Pilgrim, D. A. (1995). Automatic fish population counting by artificial neural network. Aquaculture, 133(1), 45-55.
[20] Ohba, K., Sato, Y., & Ikeuchi, K. (2000). Appearance-based visual learning and object recognition with illumination invariance. Machine Vision and Applications, 12(4), 189-196.
[21] Pan, J. S., Kong, L., Sung, T. W., Tsai, P. W., & Snášel, V. (2018). α-Fraction first strategy for hierarchical model in wireless sensor networks. Journal of Internet Technology, 19(6), 1717-1726.
[22] Pan, J. S., Lee, C. Y., Sghaier, A., Zeghid, M., & Xie, J. (2019). Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix-Vector Product Approach. IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
[23] Pistori, H., Odakura, V. V. V. A., Monteiro, J. B. O., Gonçalves, W. N., Roel, A. R., de Andrade Silva, J., & Machado, B. B. (2010). Mice and larvae tracking using a particle filter with an auto-adjustable observation model. Pattern Recognition Letters, 31(4), 337-346.
[24] Qing, Y., Xian, D. X., Liu, Q. J., Yang, B. J., Diao, G. Q., & Jian, T. A. N. G. (2014). Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture, 13(8), 1736-1745.
[25] Gonazlez, R. C. & Woods, R. E. (1992). Digital Image Processing. 2nd edition, Addison-Wesley.
[26] Silvério, F. J. L. (2016). Automatic Fish Counting in Aquariums. (Master's thesis, University of Lisbon).
[27] Zion, B., Doitch, N., Ostrovsky, V., Alchanatis, V., Segev, R., Barki, A., & Karplus, I. (2006). Ornamental Fish Fry Counting by Image Processing. Agricultural Research Organization. Bet Dagan.
[28] Zion, B. (2012). The use of computer vision technologies in aquaculture–a review. Computers and electronics in agriculture, 88, 125-132.
[29] 中華民國國家發展委員會(2009)。六大新興產業(98~101年)。中華民國國家發展委員會。取自 https://www.ndc.gov.tw/News.aspx?n=E641F7FF2AE058A1&sms=8B7FD77816422CEF
[30] 吳其勳(2011)。雲端運算徹底了解:基礎篇。取自 https://www.ithome.com.tw/article/93002
[31] 林宜弘(1995)。機器視覺應用在魚苗計數之可行性研究。農業機械學刊,4(2),37-45。
[32] 邱建欽(2016)。一個使用標記控制分水嶺與平均移動影像分割方法於自動害蟲計數之研究。國立中興大學資訊科學與工程學系碩士論文,台中市。 取自https://hdl.handle.net/11296/j59bkw
[33] 張哲維(2017)。台灣觀賞水族產業分析。國立臺灣海洋大學水產養殖學系碩士論文,基隆市。 取自https://hdl.handle.net/11296/9b7fw7
[34] 彭彥鈞(2013)。一種使用HSV色彩模型之S資訊與灰階值分割及辨識彩色影像中導盲磚區域的方法。中華大學電機工程學系碩士班碩士論文,新竹市。 取自https://hdl.handle.net/11296/tha33e
[35] 黃建華(2002)。簡易魚苗自動點算系統設計。國立中山大學海洋生物研究所碩士論文,高雄市。 取自https://hdl.handle.net/11296/6erp85
[36] 楊昌憲(2018)。具備以Python為基礎之可嵌入式投資策略的MVC框架─以程式交易系統為例。國立高雄應用科技大學資訊管理研究所碩士班碩士論文,高雄市。 取自https://hdl.handle.net/11296/jz999v
[37] 鄭逸寧(2011)。物聯網技術大剖析。取自 https://www.ithome.com.tw/news/90461.
[38] 賴丁郝(2019)。物聯網架構於雲端控制之研究探討-以Google API為例。國立宜蘭大學多媒體網路通訊數位學習碩士在職專班碩士論文,宜蘭縣。 取自https://hdl.handle.net/11296/9nps46
[39] 駱易辰(2007)。HSV色彩空間前景物體抽取及其於人體動作辨識系統應用。國立交通大學電機與控制工程系所碩士論文,新竹市。 取自https://hdl.handle.net/11296/f8duu7

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top