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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89768


    Title: 花樣口罩辨識;The patterned face mask detection
    Authors: 姜亭光;Jiang, Ting-Guang
    Contributors: 資訊工程學系在職專班
    Keywords: 影像辨識;人臉偵測;深度學習;口罩辨識;Image recognition;Face detection;Deep learning;Mask detection
    Date: 2022-09-16
    Issue Date: 2022-10-04 11:58:48 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。
    ;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.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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