花樣口罩辨識

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姓名

姜亭光(Ting-Guang Jiang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

花樣口罩辨識
(The patterned face mask detection)

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摘要(中)

由於2019年冠狀病毒COVID-19的迅速傳播，已影響了全世界，各國政府與人民正面臨著健康危機，COVID-19是一種呼吸系統疾病，可導致受影響個體出現嚴重的肺炎病例。這種疾病是通過與感染者的直接接觸，以及當感染者咳嗽、打噴嚏或將病毒呼出到空氣中時釋放的唾液珠、呼吸道飛沫而獲得的，因此世界衛生組織WHO提出建議，人們戴口罩在預防COVID-19傳播方面非常有效。

隨著近年人工智慧與電腦影像技術的發展，把相關技術應用於偵測人們是否有佩戴口罩，可以大幅降低對人力的依賴，實現有效地佩戴口罩偵測系統，並能搭配自動化警示機制，使得COVID-19傳播風險大幅下降。

目前應用於口罩辨識的方法遇上佩戴花樣與罩面是深色口罩時，偵測效果較差，因此本研究提出方法針對此問題作出改善，並提高整體辨識準確率。本論文目標是提出一套口罩辨識方法，使用深度學習的架構結合影像處理的演算法達成目標，首先透過人臉識別分類器將人臉影像資料中人臉的部分擷取出來，再利用CNN預訓練模型的基礎改進模型網路結構，經過逐漸調整並優化方法後，實驗結果顯示準確率達到95%，由此證明本方法具備一定程度之可用性。

摘要(英)

In 2019, governments and people around the world were facing health
issues due to the rapid spread of the coronavirus, a respiratory
disease that causes severe pneumonia. COVID-19 transmits when people
breathe in air contaminated by droplets and small airborne particles
containing the virus, so the World Health Organization (WHO) recommends
people wear masks to prevent the spread of COVID-19.

In recent years, with development of artificial intelligence and
computer imaging technology, the face mask-wearing detection algorithm
automatically detects masks which can decrease the waste of human
resources and creates alert which can reduce the spread of the disease.

The current method used in mask detection has low accuracy when
people wearing masks with patterns or dark-colored. Therefore, in this
study, improvement will be proposed in order to increase accuracy.

The goal of this research is to propose several mask detection methods by using the deep learning algorithm combined with the image processing algorithm. First, the face part in the image is extracted by the face recognition classifier, and then use the pre-trained CNN model as a basis to gradually improve its model network structure.

According to the experiment, the accuracy rate reaches 95%, which
proves that this method has a certain degree of accuracy after optimization.

關鍵字(中)

★ 影像辨識
★ 人臉偵測
★ 深度學習
★ 口罩辨識

關鍵字(英)

★ Image recognition
★ Face detection
★ Deep learning
★ Mask detection

論文目次

摘要 ............................................................. i
Abstract ................................................. ...... ii
致謝 ............................................................. iii
目錄 ............................................................. iv
圖目錄 ........................................................... vi
表目錄 ........................................................... viii
一、緒論 ......................................................... 1
1-1 研究背景 .................................................... 1
1-2 研究動機與目的 ............................................... 2
1-3 研究目標 .................................................... 3
1-4 論文架構 .................................................... 3
二、文獻探討 ...................................................... 4
2-1 影像分類深度學習模型 .......................................... 4
2-2 物件偵測技術 ................................................. 7
2-3 口罩辨識技術 ................................................ 15
三、解決方案 ..................................................... 19
3-1 資料前處理 .................................................. 19
3-2 CNN 模型訓練 ................................................ 21
3-3 損失函數 .................................................... 24
四、實驗設計與結果 ................................................. 25
4-1 實驗環境 .................................................... 25
4-2 人臉影像資料分析 ............................................. 25
4-3 K-fold Cross Validation .................................... 27
4-4 衡量指標 .................................................... 28
4-5 實驗一:模型訓練 .............................................. 30
4-5-1 實驗流程 ................................................ 30
4-5-2 實驗結果 ................................................ 31
4-6 實驗二:應用人臉識別分類器 ...................................... 32
4-6-1 實驗流程 ................................................ 32
4-6-2 實驗結果 ................................................ 33
4-7 實驗結果比較 ................................................. 34
4-7-1 數據比較 ................................................ 34
4-7-2 模型優化比較 ............................................. 35
4-7-3 辨識失敗特徵比較 .......................................... 36
4-8 與現有方法比較 ............................................... 40
五、結論與未來展望 ................................................. 42
5-1 結論 ........................................................ 42
5-2 未來展望 ..................................................... 43
參考文獻 ........................................................... 44

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審核日期

2022-9-16

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