結合自我注意力模塊的多尺度特徵融合網路用於場景文字偵測

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姓名

何立群(Li-Chun Ho) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

結合自我注意力模塊的多尺度特徵融合網路用於場景文字偵測
(Multi-Scale Feature Fusion Network Combined with Self-Attention Module for Scene Text Detection)

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

場景文字偵測的研究在近年來有突破性的發展，並且有著許多不同的應用，例如文件文字偵測及停車場的車牌辨識。但是，對於像是招牌、告示牌等任意形狀的場景文字偵測依然存在著許多問題，例如，許多方法沒辦法將彎曲的文字完整的標示出來，也無法有效的分開相鄰的文字。因此，我們提出了一個更有效的模型，它可以更有效的融合及利用特徵，並偵測出任意形狀的場景文字。我們是基於文字的中心區域進行預測，並透過後處理將預測出的機率圖進行擴張，得到整個文字區域的結果。我們提出Multi-Scale Feature Fusion Network以更有效的萃取及融合特徵，其中包含了結合Self-Attention Module (SAM)的Multi-Scale Attention Module (MSAM)，可以更有效的精煉特徵，最後由Self-Attention Head (SAH)預測文字機率圖。本文透過實驗證實了此方法的效果，在Total-Text數據集上得到87.4分的F-score。

摘要(英)

The research on scene text detection has made breakthroughs in recent years and has many different applications, such as document text detection and license plate recognition in parking lots. However, there are still many problems in scene text detection with arbitrary shapes such as signboards and billboards. For example, many methods cannot mark curved text fully, nor can they effectively separate adjacent text. Therefore, we propose a more efficient model, which can more effectively fuse and utilize features and detect scene texts of arbitrary shapes. In this paper, the result is predicted based on the central area of the text, and the predicted probability map is expanded through post-processing to obtain the result of the entire text area. We propose a Multi-Scale Feature Fusion Network to extract and fuse features more effectively, including Multi-Scale Attention Modules (MSAMs) combined with Self-Attention Modules (SAMs), which can refine features more effectively. Finally, Self-Attention Head (SAH) predicts the text probability map. We confirm the effect of this method through experiments and achieve F-score of 87.4 on the Total-Text dataset.

關鍵字(中)

★ 自我注意力模塊
★ 多尺度網路
★ 場景文字偵測

關鍵字(英)

★ Self-Attention Module
★ Multi-Scale Network
★ Scene Text Detection

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vii
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 2
第二章文獻回顧 3
2.1 場景文字偵測模型 3
2.1.1 邊界框(Bounding Box)的偵測方法 3
2.1.2 像素(Pixel)尺度的偵測方法 6
2.1.3 整合(Hybrid)的預測方法 9
2.1.4 核心(Kernel)的偵測方法 11
2.2 骨幹(Backbone)網路 13
2.3 自我注意力機制(Self-Attention) 14
2.4 多尺度(Multi-Scale)網路 15
2.5 輕量化方法 16
第三章研究方法與架構 19
3.1 模型架構 19
3.2 Backbone 19
3.3 Multi-Scale Feature Fusion Network 21
3.3.1 Multi-Scale Attention Module V1 (MSAM V1) 23
3.3.2 Multi-Scale Attention Module V2 (MSAM V2) 25
3.3.3 Convolution Block 27
3.4 Self-Attention Head (SAH) 28
3.5 Loss Functions and Post Processing 28
第四章實驗結果 30
4.1 資料集與評估方法 30
4.2 開發環境 32
4.3 消融實驗 33
4.4 實驗數據 35
第五章結論與未來展望 40
參考文獻 41

參考文獻

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指導教授

范國清、謝君偉
(Kuo-Chin Fan、Jun-Wei Hsieh)

審核日期

2022-7-28

推文