基於多尺度預測和循環對抗網路的招牌檢測與識別方法之研製

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姓名

林冠宏(Kuan-Hung Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於多尺度預測和循環對抗網路的招牌檢測與識別方法之研製
(Signboard detection and recognition deep learning modeling based on multiscale prediction and CycleGAN)

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摘要(中)

物件偵測在電腦視覺任務上是一個很熱門的領域，此技術被使用在許多領域上。為了在提高預測精確度的同時也要保證執行速度在物件檢測上是一個很大的挑戰。有許多專家、學者已致力於這項任務上並提出了許多方法，使得物件檢測的方法日益成熟。
在物件檢測中的大多資料集其背景相當複雜，使得模型沒有檢測到目標物件或者發生誤判的情形，為了要解決檢測遺漏有許多方法被提出，例如特徵金字塔網路、多尺度預測和注意力模組等，但極少有方法用以解決將背景誤判為目標物件上。在本文中我們提出了一個兩階段訓練方式的物件檢測模型，用以使用在臺灣街景招牌資料集上，此方法添加了部份語意分割技巧且無須使用到像素間的標記，解決由於大多招牌形狀極為相似而引發的誤判情況。此外我們將此方法進一步的改良使其成為一階段的物件檢測模型，使它的預測結果更加穩定且易於訓練。

摘要(英)

Object detection is a popular computer vision task in deep learning and the technique is widely used in many fields. To improve the precision of the models while ensuring the inference time is a big challenge. Many experts and scholars have invested in this works and proposed lots of methods to solve this problem, making object detection become more and more mature.
The scenes in most object detection datasets are very complicated so that the model cannot detect the objects or it might regard background as an object. To conquer miss detection, lots of methods are proposed like Feature Pyramid Network, multi-scales prediction and attention module. However, there are few methods to prevent the models from misjudging non-objects to objects. In this thesis, we propose a two-phase training method used for Taiwan Street View Signboard Dataset. The model is added with some techniques from segmentation without pixel-to-pixel labeling, solving misjudgments caused by the similar shapes of various signboards. We further improve the method into a one-stage detection model, make the model to be more stable and easier for training.

關鍵字(中)

★ 深度學習
★ 物件檢測
★ 招牌辨識

關鍵字(英)

★ deep learning
★ object detection
★ signboard recognition

論文目次

1 Introduction 1
2 Related work 3
2.1 Features Extraction Methods 3
2.1.1 AlexNet 3
2.1.2 VGGNet 4
2.1.3 Residual Neural Network 5
2.2 Object Detection 6
2.2.1 Two-Stage Detector 7
2.2.2 One-Stage Detector 11
2.3 Segmentation 19
2.3.1 Fully Convolutional Networks 19
2.3.2 U-Net 20
3 Proposed Method 21
3.1 Two-Phase Training Methods 21
3.1.1 Bounding Boxes Proposal 21
3.1.2 CycleGAN 23
3.1.3 Region Category Checking 25
3.2 Proposed One-Stage Detector 32
3.2.1 Multi-Scales Prediction 32
3.2.2 Ground Truth for Segmentation 34
3.2.3 Segmentation Approach 35
4 Experimental Results 40
4.1 Datasets 40
4.2 Training 41
4.3 Testing 42
5 Conclusion 45
6 Reference 46

參考文獻

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指導教授

施國琛(Timothy K Shih)

審核日期

2021-1-15

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