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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Yue-Shao Chen | en |
| dc.contributor.author | 陳約劭 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:04:04Z | - |
| dc.date.available | 2024-08-11 | |
| dc.date.copyright | 2021-11-09 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-18 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81808 | - |
| dc.description.abstract | "在亞熱帶乳牛產業中，熱緊迫為乳牛飼養與生產管理的重要問題。熱緊迫會影響乳牛的採食量、飲水量、生育能力、呼吸速率和產乳量。在這些行為中，乳牛採食量的變化為乳牛熱緊迫的重要指標，亦直接影響乳牛之產乳量。本研究利用小樣本學習之臉部辨識演算法監測乳牛採食行為，以解決傳統模型遇到新類別就需花費大量時間重新訓練的問題，同時提升辨識效能。影像系統使用Raspberry Pi 3B+作為邊緣運算系統，搭配ArduCam攝影鏡頭來擷取乳牛採食時之臉部影像進行臉部辨識。乳牛採食行為辨識有兩步驟：偵測及辨識。YOLOv4-Tiny用於偵測乳牛臉部位置，模型F1-score為0.98。臉部辨識模型以小樣本學習演算法訓練MobileNetV2，利用online triplet loss損失函數來實現。模型輸出特徵向量之L2距離即為相似度，且可用距離閾值判斷是否為新目標牛隻，其F1-score為0.91。本研究驗證實驗在辨識基準模型訓練19類新增5類的情況下，各類100張訓練影像即可達到平均準確率0.97。最終驗證系統預測個別牛採食時間與人工計算其R^2=0.98。本研究亦利用所開發之牛臉辨識模型加入牛眼偵測模型，同樣以YOLOv4-Tiny為模型架構，其表現F1-score為0.92，再利用熱影像儀擷取個別牛眼溫度資訊，其溫度範圍落在±0.3°C內。本研究進一步將自動辨識系統所得個別泌乳牛採食時間監測資料，以分娩後天數區分為三個類別分析個別乳牛採食時間與溫濕度指標(Temperature and humidity index, THI)關聯性。分析結果顯示離分娩日期越短的牛隻其所受THI影響較小，而泌乳中至後期之牛隻，2日前平均THI對於採食時間有負相關性。牛眼溫度監測中得到所收溫度資訊受環境溫度影響，其R^2=0.89，而在相同環境溫度時偵測牛眼溫度範圍穩定，未來可應用於乳牛異常溫度之偵測。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:04:04Z (GMT). No. of bitstreams: 1 U0001-2607202117500000.pdf: 5184453 bytes, checksum: e78315eab377176eef74a42ee26b72a6 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 ix 表目錄 xiv 第一章 緒論 1 1.1 前言 1 1.2 研究目的 5 第二章 文獻探討 7 2.1 熱緊迫對泌乳牛的影響 7 2.1.1 熱緊迫對泌乳牛產乳量影響 7 2.1.2 熱緊迫對牛隻行為影響 8 2.2 泌乳牛行為監測與分析 9 2.2.1 接觸式監測系統 9 2.2.2 非接觸式監測系統 9 2.3 深度學習應用於物體辨識 10 2.4 個體臉部辨識 10 2.4.1 線性演算法 11 2.4.2 非線性演算法 11 2.4.3 深度學習 12 2.5 小樣本學習演算法 14 2.5.1 特徵向量學習演算法 14 2.6 熱成像監測分析 17 2.6.1 深度學習應用於熱成像 17 2.6.2 熱成像溫度監測分析 18 第三章 研究方法 20 3.1 實驗動物與實驗場域 20 3.1.1 泌乳牛 20 3.1.2 實驗場域 20 3.1.3 台大牧場泌乳牛餵食規則 21 3.2 影像監測系統 21 3.2.1 系統架構 21 3.2.2 嵌入式開發版 23 3.2.3 影像模組 24 3.2.4 系統硬體模組 24 3.3 訓練影像資料集 26 3.3.1 牛臉偵測影像資料 26 3.3.2 牛臉辨識影像資料 27 3.3.3 牛眼偵測影像資料 29 3.4 影像監測系統軟體架構 30 3.4.1 採食監測演算法架構 30 3.4.2 牛臉偵測演算法 35 3.4.3 牛臉辨識演算法 37 3.4.4 小樣本學習訓練流程 40 3.4.5 軟體架構與實作說明 43 3.5 乳牛熱影像監測實驗 47 3.5.1 牛眼溫度監測流程 47 3.5.2 熱成像監測模組 49 3.5.3 牛眼偵測模型訓練 50 第四章 結果與討論 51 4.1採食影像監測系統 51 4.2採食監測軟體成果 52 4.3牛臉偵測結果 54 4.3.1牛臉偵測模型訓練 54 4.3.2 牛臉偵測模型測試 58 4.3.3 群體牛採食時間驗證 61 4.4個別牛臉辨識結果 62 4.4.1 InceptionV3 Triplet mining策略試驗 62 4.4.2 MobileNetV2初步訓練試驗結果 65 4.4.3 MobileNetV2小樣本訓練驗證 66 4.4.4 個別牛採食時間驗證 74 4.5 採食系統自動化更新驗證 75 4.5.1 預測新牛隻結果 75 4.5.2 加入新牛隻更新辨識模型結果 77 4.6 採食結果分析 81 4.6.1 群體牛隻採食結果分析 81 4.6.2 個別牛隻採食結果分析 84 4.7 熱影像溫度結果分析 93 4.7.1 牛眼偵測模型結果 93 4.7.2 牛眼溫度偵測結果分析 95 第五章 結論與建議 102 5.1 結論 102 5.2 建議 104 參考文獻 105 | |
| dc.language.iso | zh-TW | |
| dc.subject | 熱緊迫 | zh_TW |
| dc.subject | 嵌入式系統 | zh_TW |
| dc.subject | 小樣本學習 | zh_TW |
| dc.subject | 臉部辨識 | zh_TW |
| dc.subject | 牛眼溫度 | zh_TW |
| dc.subject | cow eye temperature | en |
| dc.subject | heat stress | en |
| dc.subject | embedded system | en |
| dc.subject | few-shot learning | en |
| dc.subject | face recognition | en |
| dc.title | 小樣本學習臉部辨識演算法應用於乳牛採食與溫度監測 | zh_TW |
| dc.title | Dairy Cow Face Recognition Based on Few-Shot Learning for Feeding Behavior and Eye Temperature Monitoring | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 徐濟泰(Hsin-Tsai Liu),陳世芳(Chih-Yang Tseng) | |
| dc.subject.keyword | 熱緊迫,嵌入式系統,小樣本學習,臉部辨識,牛眼溫度, | zh_TW |
| dc.subject.keyword | heat stress,embedded system,few-shot learning,face recognition,cow eye temperature, | en |
| dc.relation.page | 112 | |
| dc.identifier.doi | 10.6342/NTU202101767 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-19 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| dc.date.embargo-lift | 2024-08-11 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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