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.