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姓名 陳昱銘(Yu-Ming Chen) 查詢紙本館藏 畢業系所 資訊工程學系 論文名稱 應用增量式學習於多種農作物判釋之研究
(Application of Incremental Learning for Multiple Crop Interpretation)檔案 [Endnote RIS 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 ( 永不開放)
摘要(中) 航空攝影技術廣泛應用於農業調查，結合深度學習技術，可以解析高
精度的航空照片內容，協助調查人員進行農地面積計算和農作坵塊的農作
物分類，進而瞭解農業土地利用現況，節省人力和調查時間。然而，訓練
資料的收集通常非常耗時，且目前的資料集是在多年的時間裡逐一收集而
成。在這樣的背景下，目前已有多模型架構的方法來解決這個問題，但這
些方法需要大量的推論時間。在實際應用中，使用者往往無法接受如此長
的推論時間。因此，本研究引入增量式學習的方法來解決推論時間過長的
問題。增量式學習允許模型在不重新訓練的情況下，接受新的訓練資料並
進行更新，從而節省了推論時間。通過增量式學習，我們可以有效地利用
已有的模型知識，並隨著新的資料進行模型更新，以適應新的農作物類型
和土地利用情況。透過引入增量式學習的方法，我們可以在不犧牲準確性
的情況下，實現更快速的推論時間，從而使航空攝影技術在農業調查中更
具實用性和效率。摘要(英) Aerial photography technology is widely used in agricultural surveys, and
when combined with deep learning techniques, it can provide detailed interpretation of high-resolution aerial images. This assists surveyors in calculating agricultural land areas and classifying crops in agricultural parcels, thereby gaining
insights into agricultural land use patterns and saving manpower and survey time.
However, the collection of training data for such applications is time-consuming
and often spans multiple years. In this context, existing approaches have employed
multi-model architectures to address the problem. However, these methods require
significant inference time, which is not acceptable for practical use by end-users.
To overcome the issue of prolonged inference time, this study introduces an incremental machine learning approach. Incremental learning allows the model to accept new training data and update itself without retraining, thereby reducing inference time. By leveraging incremental learning, we can effectively utilize existing
model knowledge and update the model with new data to adapt to new crop types
and land use conditions. By incorporating incremental learning, we can achieve
faster inference time without compromising accuracy, making aerial photography
technology more practical and efficient in agricultural surveys.關鍵字(中) ★ 航空影像
★ 語意分割
★ 增進式學習關鍵字(英) ★ Aerial image
★ Semantic segmentation
★ Incremental learning論文目次 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
一 、 緒論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1-1 研究背景 . . . . . . . . . . . . . . . . . . . . . . . . . 1
1-2 研究動機與目的 . . . . . . . . . . . . . . . . . . . . . 3
1-3 問題定義 . . . . . . . . . . . . . . . . . . . . . . . . . 4
1-4 研究貢獻 . . . . . . . . . . . . . . . . . . . . . . . . . 5
1-5 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . 5
二 、 相關研究 . . . . . . . . . . . . . . . . . . . . . . . . . 6
2-1 水稻語意分割模型 UNet-VGG16 . . . . . . . . . . . . 6
2-2 多種農作物語義分割集成模型 . . . . . . . . . . . . . 6
2-3 增量式學習 . . . . . . . . . . . . . . . . . . . . . . . . 7
2-4 災難性遺忘 . . . . . . . . . . . . . . . . . . . . . . . . 7
三 、 研究方法 . . . . . . . . . . . . . . . . . . . . . . . . . 9
iii
3-1 資料前處理 . . . . . . . . . . . . . . . . . . . . . . . . 10
3-1-1 產生 pseudo-data . . . . . . . . . . . . . . . . . . . . . 10
3-1-2 結合地真資料與 pseudo-data . . . . . . . . . . . . . . . 12
3-2 模型訓練過程 . . . . . . . . . . . . . . . . . . . . . . 13
3-2-1 建立客製化 Distiller . . . . . . . . . . . . . . . . . . . 13
3-2-2 修改 Distillation Loss . . . . . . . . . . . . . . . . . . . 15
3-2-3 網路架構 Unet-VGG16(5*5) . . . . . . . . . . . . . . . 16
四 、 實驗與結果討論 . . . . . . . . . . . . . . . . . . . . . 18
4-1 資料集介紹 . . . . . . . . . . . . . . . . . . . . . . . . 18
4-1-1 航空影像 . . . . . . . . . . . . . . . . . . . . . . . . . 18
4-1-2 地真資料 . . . . . . . . . . . . . . . . . . . . . . . . . 19
4-1-3 模型訓練資料 . . . . . . . . . . . . . . . . . . . . . . 20
4-2 實驗前準備 . . . . . . . . . . . . . . . . . . . . . . . . 22
4-3 模型效能評估方法與性能檢定 . . . . . . . . . . . . . 23
4-3-1 Parcel-based kappa with area weight . . . . . . . . . . . 23
4-3-2 Wilcoxon sign rank Test . . . . . . . . . . . . . . . . . . 25
4-4 實驗一: 建立 UNet-VGG16 增量式學習模型 . . . . . . 25
4-4-1 動機與目的 . . . . . . . . . . . . . . . . . . . . . . . . 25
4-4-2 實驗方法 . . . . . . . . . . . . . . . . . . . . . . . . . 26
4-4-3 實驗結果 . . . . . . . . . . . . . . . . . . . . . . . . . 29
4-5 實驗二: 建立 UNet-VGG16(5*5) 增量式學習模型 . . . 35
4-5-1 動機與目的 . . . . . . . . . . . . . . . . . . . . . . . . 35
4-5-2 實驗方法 . . . . . . . . . . . . . . . . . . . . . . . . . 35
4-5-3 實驗結果 . . . . . . . . . . . . . . . . . . . . . . . . . 36
iv
五 、 結論與未來展望 . . . . . . . . . . . . . . . . . . . . . 41
5-1 論文總結論 . . . . . . . . . . . . . . . . . . . . . . . . 41
5-2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . 41
參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43參考文獻 [1] Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, and
Barbara Caputo, “Modeling the Background for Incremental Learning in
Semantic Segmentation,” in 2020 IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020, pp. 9230–
9239, IEEE.
[2] 李治逸、王韻皓, “航空攝影技術應用於農業資訊調查之介紹,”
https://www.coa.gov.tw/ws.php?id=2445930, Accessed June 1, 2023.
[3] “農 林 漁 牧 產 品 生 產 值,” https://agrstat.coa.gov.tw/sdweb/
public/book/Book_File.ashx?chapter_id=1385_22_2, Accessed June
1, 2023.
[4] Adventus Ridwan Kis Sugiyarto, “The ensemble model for semantic segmentation of multi-crops: Rice and cabbage,” 碩士論文, 國立中央大學, 民
國 111 年 6 月.
[5] Ida Wahyuni, Wei-Jen Wang, Deron Liang, and Chin-Chun Chang, “Rice Semantic Segmentation Using Unet-VGG16: A Case Study in Yunlin, Taiwan,”
in 2021 International Symposium on Intelligent Signal Processing and Com43
munication Systems (ISPACS), Hualien City, Taiwan, Nov. 2021, pp. 1–2,
IEEE.
[6] Olaf Ronneberger, Philipp Fischer, and Thomas Brox, “U-net: Convolutional
networks for biomedical image segmentation,” in International Conference
on Medical Image Computing and Computer-Assisted Intervention, 2015, pp.
234–241.
[7] Karen Simonyan and Andrew Zisserman, “Very deep convolutional networks
for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[8] Sylvestre-Armand Rebuffi, Alexander Kolesnikov, Georg Sperl, and
Christoph H Lampert, “icarl: Incremental classifier and representation learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition. IEEE, 2017, pp. 2001–2010.
[9] Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and DeChuan Zhan, “Forward Compatible Few-Shot Class-Incremental Learning,”
in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), New Orleans, LA, USA, June 2022, pp. 9036–9046, IEEE.
[10] Michael McCloskey and Neal J Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of
learning and motivation, vol. 24, pp. 109–165. Elsevier, 1989.
[11] Tao Feng, Mang Wang, and Hangjie Yuan, “Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation,”
44
in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), New Orleans, LA, USA, June 2022, pp. 9417–9426, IEEE.
[12] Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach,
and Tinne Tuytelaars, “Memory Aware Synapses: Learning What (not)
to Forget,” in Computer Vision – ECCV 2018, Vittorio Ferrari, Martial
Hebert, Cristian Sminchisescu, and Yair Weiss, Eds., vol. 11207, pp. 144–
161. Springer International Publishing, Cham, 2018.
[13] Arun Mallya and Svetlana Lazebnik, “PackNet: Adding Multiple Tasks to
a Single Network by Iterative Pruning,” in 2018 IEEE/CVF Conference on
Computer Vision and Pattern Recognition, Salt Lake City, UT, June 2018,
pp. 7765–7773, IEEE.
[14] Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean, “Distilling the knowledge
in a neural network,” in NIPS Deep Learning and Representation Learning
Workshop, 2015.
[15] Jacob Cohen, “A coefficient of agreement for nominal scales,” Educational
and Psychological Measurement, vol. 20, no. 1, pp. 37–46, 1960.
[16] J. Richard Landis and Gary G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977.
[17] Frank Wilcoxon, “Individual comparisons by ranking methods,” Biometrics
Bulletin, vol. 1, no. 6, pp. 80–83, 1945.指導教授 梁德容 張欽圳 王尉任(Deron Liang Chin-Chun Chang Wei-Jen Wang) 審核日期 2023-7-31 推文 plurk
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