臺灣博碩士論文加值系統

COVID-19是一種通過呼吸道飛沫傳播的病毒，而戴口罩可以減少呼吸道飛沫在空氣中的擴散，因此即使戴口罩的人感染了COVID-19，口罩也可以減少病毒釋放到空氣中的量，降低其他人感染的風險，減少病毒傳播的速度。世界衛生組織為保護在疫情最前線的醫護人員安全，規範了醫護人員在工作時必須穿著個人防護裝備來保護他們自身的安全。口罩是最基本的個人防護裝備，然而在許多檢查口罩是否適切配戴的管制點，如醫院、火車捷運等大眾運輸工具入口，往往需要能快速自動辨識民眾是否適切配戴口罩的資訊系統。因此，在本論文中我們提出使用深度學習物件偵測技術，對即時攝影影片中的人臉進行偵測，並進一步辨識人臉分為正確配戴口罩、無配戴口罩、未正確配戴口罩等三種類型，再對無配戴口罩與未正確配戴口罩發出警示，藉此來提高口罩配戴適切度。我們測試YOLOv5與YOLOv7兩種最新物件偵測技術在此應用的辨識效能。實驗結果顯示YOLOv7的辨識效能較佳，對正確配戴口罩、無配戴口罩、未正確配戴口罩等三種類型的人臉辨識準確度分別為99.6%、99.5%與99.6%。

COVID-19 is a virus that spreads through respiratory droplets, wearing a mask can reduce the spread of respiratory droplets in the air, so even if the person wearing the mask is infected with COVID-19, the mask can reduce the spread of the virus released into the air, reducing the risk of infecting others and slowing down the rate at which the virus spreads. In order to protect the safety of medical staff at the forefront of the epidemic, the World Health Organization stipulates that medical staff must wear personal protective equipment when working to ensure their own safety. Masks are the most basic personal protective equipment. However, many control points for checking whether masks are suitably put on at the entrances and exits of public transportation such as hospitals, trains, and subways often require an information system that can quickly identify whether individuals are wearing masks appropriately. Therefore, in this thesis, we propose an approach to detect faces in real-time photography videos using deep learning, and further classify detected faces into three types: correctly wearing a mask, not wearing a mask, and incorrectly wearing a mask. The system further warns against people who do not wear masks and who are not wearing masks correctly, thereby improving the suitability of masks. We tested two state-of-the-art object detection techniques, YOLOv5 and YOLOv7, to show the performance of the proposed system. The experimental results show that the performance of YOLOv7 is better, and the recognition accuracy rates of the three types of faces that wear masks correctly, do not wear masks, and wear masks incorrectly are 92.4%, 94.7%, and 98.9%, respectively.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 V
表目錄 VI
第一章 緒論 1
第二章 相關研究 3
第三章 研究方法 4
3.1 YOLO起源 4
3.2 EFFICIENTNET網路架構 5
3.3 YOLOV5 5
3.4 YOLOV7 8
第四章 系統架構 11
第五章 實驗與系統實作 13
5.1 實驗環境 13
5.2 人臉與口罩影像資料集 13
5.3 人臉預訓練資料集 15
5.4效能評估 15
5.5實驗結果 19
第六章 結論 27
參考文獻 28

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