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研究生:王琮雄
研究生(外文):WANG, TSUNG-HSIUNG
論文名稱:基於區塊方式水果表面缺陷等級分類方法
論文名稱(外文):Patch-based Skin Defect Identification Method for Fruit Grading
指導教授:葉奕成葉奕成引用關係
指導教授(外文):YEH, I-CHENG
口試委員:黃仲誼簡廷因
口試委員(外文):HUNAG, CHUNG-ICHIEN, TING-YING
口試日期:2021-01-22
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:31
中文關鍵詞:區塊化影像辨識物件偵測與分類特徵工程
外文關鍵詞:Patch-based image classificationObject detection and classificationFeature engineering
相關次數:
  • 被引用被引用:1
  • 點閱點閱:356
  • 評分評分:
  • 下載下載:58
  • 收藏至我的研究室書目清單書目收藏:0
現今許多不同產業或生活中皆導入一些有關人工智慧與影像辨識的相關技術,可以減少許多人力與時間提升決策的速度與可靠度。在此篇研究中我們,提出一套基於物件區塊訓練方式。這裡我們利用芒果作為我們主要的資料集,當中根據不同的外觀品質可以分為三個等級,以及不同程度的五種瑕疵類別。以辨別芒果為例,人類在辨識芒果等級或瑕疵時常常以某區塊表面特徵為辨識依據,因此本論文有別於其他研究成果直接將整張圖片作為卷積神經網路輸入來分類的方式,提出一種以區塊的基礎的分類方法來辨別水果的瑕疵與分級。本論文根據超像素分群技術,將芒果主體影像拆分成數個區塊,並根據這些區塊進一步處理取得一些統計特徵,像是計算出缺陷區塊面積比例與數量、代表色、平均顏色等。後續再針對瑕疵類別基於知識蒸餾的神經網路框架進行芒果瑕疵種類辨識來分辨各區塊之品質。最終運用這些基於區塊的簡易統計特徵向量來訓練芒果等級分類器。根據芒果資料集實驗結果顯示,利用本論文所提出的統計特徵向量來預測芒果等級可以達到一定的準確度。如果進一步將我們所得到的特徵向量與ResNet50網路提取的芒果影像特徵結合後訓練,即便該向量在整體特徵中佔比不高亦能明顯提升訓練模型的預測能力。預期這套方法不只能應用在芒果,也可以應用在像是麵包、其他水果、或醫療影像中物件形狀與結構較無特定規則的自然物件分類。

關鍵字:區塊化影像辨識、物件偵測與分類、特徵工程

Nowadays, there are many different industries and life situations where the introduction of technologies related to artificial intelligence and image recognition can reduce labor and time to improve the speed and reliability of decision making. In this study, we propose a patch-based training approach. We use mangoes as our primary dataset, which can be classified into three classes according to their appearance quality and five types of defects of different degrees. In this thesis, we propose a patch-based classification method to identify and classify the defects of mangoes, which is different from other research results that directly use the whole picture as input to a convolutional neural network. We first use clustering to divide the mango into several superpixels as patches of mango, and we can further process these patches to obtain some statistical features, such as the proportion of defective parts on the surface, representative color, average color, etc. We use a knowledge distillation-based neural network framework for five types of mango defects classification training to obtain the target defect proportion. Finally, we can use these simple statistical feature vectors for image classification. According to the experimental results on the mango dataset, the statistical feature vector proposed in this paper can be used to predict the mango grade with a certain degree of accuracy. If we further combine proposed statistical feature vectors with the traditional color features extracted from the ResNet50 network, the predictive power of the trained model can also be improved. It is expected that this method can be applied not only to mangoes but also to bread, other fruits, or medical images, where the shape and structure of objects are not regular.

Keywords: Patch-based image classification, Object detection and classification, Feature engineering
書名頁 i
論文口試委員審定書 ii
摘要 iii
ABSTRACT iv
誌謝 v
目錄 vi
圖目錄 viii
表目錄 x
第一章、緒論 1
1.1 研究背景與動機 1
1.2 章節概要 3
第二章、文獻研究 4
2.1 物件偵測 4
2.2 區塊化影像辨識 5
2.3 知識蒸餾 6
2.4 商品辨識 8
2.5 水果辨識 9
第三章、方法 11
3.1 資料集簡介 11
3.2 研究方法概覽 11
3.3 資料前處理 12
3.3.1 三分類資料集的建立過程 13
(1)取得顯著圖(Saliency Map) 13
(2)物件區塊化 13
(3)圖像修復 14
(4)好壞區塊重新分類 16
3.3.2 六分類資料集建立過程與分類器訓練 16
3.4 芒果等級預測 19
第四章、驗證 22
4.1 實驗規劃 22
4.2 實驗步驟 22
(1)單一簡易統計特徵測試 22
(2)代表色數量交叉測試 23
(3)特徵提取網路測試 24
(4)簡易統計特徵與ResNet50特徵結合測試 25
4.3 實驗結果與討論 27
第五章、結論 28
參考文獻 29


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