地點資料庫中錯誤場景影像自動偵測系統

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姓名

柳翔元(Hsiang-Yuan Liu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

地點資料庫中錯誤場景影像自動偵測系統
(Automatic Incorrect Scene Detection System for Large Scale Location Database)

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摘要(中)

近年來由於深度學習網路的蓬勃發展，被廣泛應用於電腦視覺與圖形別的領域中，也因為人工智慧的發展，透過建置智慧化的系統，能夠有效幫助人類處理簡單且重複性高的問題．本篇論文實作一個自動化錯誤場景偵測系統，能夠有效取代以往以人工方式檢測地標圖像資料庫之正確性的過程，以節省人力成本．
在自動化錯誤場景偵測的系統中，我們提出了錯誤場景偵測演算法，以解決偵測錯誤場景的問題，並且基於此系統架構，我們提出了多級別特徵擷取器(Multiple Level Extractor)，透過擷取場景圖中不同級別的特徵，改善了Resnet50網路架構的特徵提取效果，以及多尺度距離度量(Multiple Scale Distance Measurement)，在給定的特徵擷取器之下，總和了在多種不同尺度下之特徵距離，能夠將系統之效能再提升．
最後，基於本系統的架構下，我們實驗了系統在不同的特徵擷取器與距離度量方式之下，影響系統效能之變化程度．

摘要(英)

In recent years, due to the vigorous development of deep learning networks, deep learning has been widely used in the field of computer vision and graphic recognition. Because of the development of artificial intelligence, through the establishment of intelligent systems, it can effectively help humans to handle simple and repetitive problem.
We propose an automated incorrect scene detection system. which can effectively replace the process of manually detecting the correctness of the landmark image database to save human resources costs.
In the system of automatic incorrect scene detection, we propose an incorrect scene detection algorithm to solve the problem of detecting incorrect scenes, and based on this system architecture, we propose a MLE (Multiple Level Extractor). By extracting different levels of features in the scene image, improved the feature extraction effect of the Resnet50 network architecture. In addition, we also propose MSD(Multiple Scale Distance) measurement, which sums the feature distances at various scales under a given feature extractor. MSD also improved the performance of the system. Finally, based on the architecture of the system, we experimented with the system under different feature extractor and distance measurement methods , which affect the degree of system performance change.

關鍵字(中)

★ 深度學習
★ 卷積神經網路
★ 場景識別

關鍵字(英)

★ Deep Learning
★ CNN
★ Scene Recognition

論文目次

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 . 表目錄 .
一、緒論 1
1-1 研究動機 1
1-2 相關研究 2
1-3 系統架構介紹 3
1-4 主要貢獻 3
二、地標資料庫描述與評估指標 5
2-1 地標資料庫 5
2-2 評估指標 6
2-2-1 模擬使用者上傳錯誤地標圖 6
2-2-2 ROC Curve 7
三、研究方法 9
3-1 特徵擷取階段 9
3-1-1 訓練資料集 9
3-1-2 BaseExtractor 9
3-1-3 MLE-MultipleLevelExtractor 10
3-2 圖片特徵距離度量階段 12
3-2-1 OverallDistance 12
3-2-2 MSD-MultiScaleDistance 13
3-3 錯誤場景偵測階段 14
四、實驗結果 16
4-1 實驗環境 16
4-2 比較預訓練 Resnet50 與使用地標資料庫訓練之 Resnet50 16
4-3 特徵擷取器使用不同損失函式訓練之比較 19
4-4 Baseline、MLE與MSD之比較 19
4-5 錯誤場景偵測演算法於不同的匹配場景數量 v 下之影響 19
4-6 使用 Multiple Scale Distance 度量於不同閥值 δ 下之比較 22
4-7 使用 Multiple Scale Distance 度量於不同尺度參數 r 下之比較 23
五、結論 25
參考文獻 27

參考文獻

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指導教授

鄭旭詠(HSU-YUNG CHENG)

審核日期

2019-7-16

推文