基於深度學習之盲人穿搭輔助系統

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賴映如(Ying-Ru Lai) 查詢紙本館藏

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資訊工程學系

論文名稱

基於深度學習之盲人穿搭輔助系統
(A Deep-learning-based Outfit Improvement System for Visually Impaired Persons)

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摘要(中)

視障者在日常生活常常會面臨許多的困難，尤其在衣著方面，他們除了難以自行得知衣物布料的顏色、花紋和款式等資訊之外，要如何適切地掌握衣物間互相的搭配更是個挑戰。因此，如何使視障者能在無旁人輔助的狀態下，瞭解目前全身衣物的布料資訊，並對其色彩與花紋搭配合適程度有所認知，避免因缺乏資訊而穿著突兀或不適當，是個重要的研究課題。因此，本論文結合深度學習與影像處理技術來開發一套盲人穿搭輔助系統，幫助視障者了解選用的衣物資訊與搭配合宜度。
本篇論文系統主要包含以下四個模組: (1) 骨架偵測模組: 使用 OpenPose 模型偵測與提醒拍攝是否為完整正面全身照、(2) 肢體偵測與切割處理模組: 透過CDCLHuman-Part-Segmentation模型取得肢體遮罩，並依據相對應欲判斷衣物部位做聚焦切割處理、(3) 衣物特徵辨識模組: 以深度學習技術訓練模型辨識衣物資訊與布料樣式、(4) 布料色系與飽和度辨識模組: 透過影像處理識別布料的顏色資訊。綜合上述四部分之模組功能，在取得衣物特徵、布料色系與花紋樣式等資訊後，系統透過語音提示的方式來輔助視障者能夠獨立了解他們選用的各衣物特徵，並給予穿搭參考建議，希望使他們能對於衣物搭配組合後的效果更有概念和依據，同時保有自己對搭配美感的彈性。
根據系統實驗結果顯示，三種衣物特徵以及布料款式辨識率皆在 95%以上，色系分類準確率達 87%，搭配結果建議與市調相符度約達 77.6%，由此可證明本系統具備一定程度之可用性。

摘要(英)

The visually impaired people face many difficulties in daily life, especially in clothing identification and matching. In addition to having difficulty in independently knowing the color, pattern, style and other information of clothing fabrics, it is also a challenge for them to match each other well. Therefore, how to let the visually impaired understand the fabric information what they are wearing, and the suitability of the color and pattern of their clothes without the help of others, so as to avoid conflicts or unexpected clothes matching caused by insufficient information, is an important research topic. This research uses deep learning and image processing technology to develop an outfit improvement system in order to help the visually impaired understand the information about their clothes and improve the outfit collocation.
The deep-learning-based outfit improvement system developed in this paper mainly includes: (1) Skeleton detection: Use the OpenPose model to detect and remind the user whether the photo is a frontal full-body photo, (2) Human body part detection and segmentation processing: Obtain the human body mask through the CDCL Human-Part-Segmentation model, and perform focus cutting processing according to the corresponding parts of the clothing that need to be judged, (3) Clothing feature recognition: Use deep learning technology to train the model to recognize clothing information and fabric styles, (4) Recognition of fabric color system and saturation: Recognize the color information of fabric through image processing.
Through the functions of the above four parts, after obtaining clothing features and fabric color system and pattern style information, voice prompts will be used to assist the visually impaired to independently understand the features of each clothing they choose, and to give non-absolute wear reference suggestions to help them have a more conceptual and basis for the effect of clothing collocation and combination, while maintaining their flexibility in the beauty of collocation.
According to the system experiment results, the accracy of three kinds of clothing features and fabric styles has all reached more than 95%, the color classification accuracy rate is 87%, and the match between the recommended matching results and the taste of the general public is 77.6%, which can prove that the system has a certain degree of usability.

關鍵字(中)

★ 深度學習
★ 影像辨識
★ 色彩配色
★ 視障者

關鍵字(英)

★ deep learning
★ image recognition
★ color combination
★ visually impaired

論文目次

摘要 i
ABSTRACT iii
致謝 v
目錄 vi
圖目錄 viii
表目錄 x
第一章、緒論 1
1-1 研究動機 1
1-2 研究目的 2
1-3 論文架構 3
第二章、相關研究 4
2-1 穿搭辨識與推薦 4
2-1-1 盲人穿搭輔具 4
2-1-2 服裝辨識與搭配相關研究 6
2-2 色彩與穿搭美學 10
2-2-1 色彩系統 10
2-2-2 色彩配色與服裝搭配 11
2-3 深度學習相關研究 13
2-3-1 ResNet 13
2-3-2 EfficientNet 16
2-3-3 OpenPose 20
2-3-4 Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation 22
第三章、研究方法 25
3-1 演算流程 25
3-2 肢體偵測 26
3-3 衣物特徵辨識 29
3-4 布料樣式與色系判別 31
3-4-1 布料資料前處理 31
3-4-2 布料樣式判別 33
3-4-3 布料色系與飽和度判別 34
3-5 穿搭結果評估 36
第四章、實驗設計與結果 37
4-1 肢體偵測實驗 37
4-1-1 資料集調整實驗 37
4-1-2 資料集調整實驗結果與分析 38
4-1-3 肢體偵測實驗 39
4-2 衣物特徵辨識實驗 41
4-2-1 下身種類辨識實驗 42
4-2-2 上衣袖長辨識實驗 44
4-2-3 外套辨識實驗 46
4-3 布料樣式與色系分類實驗 47
4-3-1 布料樣式實驗 48
4-3-2 布料色系與飽和度實驗 50
4-4 穿搭結果評估 52
4-4-1 穿搭評估實驗設計 52
4-4-2 穿搭評估實驗結果與分析 53
第五章、結論與未來展望 56
5-1 結論 56
5-2 未來展望 57
參考文獻 58
附錄A 62

參考文獻

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指導教授

蘇木春(Mu-Chun Su)

審核日期

2022-4-7

推文