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Ways to Wear a Mask or a Respirator (WWMR-DB)

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ieee-dataport.org2025-03-22 收录
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The Ways To Wear a Mask or a Respirator Database (WWMR-DB) is a test database that can be used to compare the behavior of current mask detection systems with images that most closely resemble the real case. It consists of 1222 images divided into 8 classes, depicting the most common ways in which masks or respirators are worn:- Mask Or Respirator Not Worn- Mask Or Respirator Correctly Worn- Mask Or Respirator Under The Nose- Mask Or Respirator Under The Chin- Mask Or Respirator Hanging From An Ear- Mask Or Respirator On The Tip Of The Nose- Mask Or Respirator On The Forehead- Mask Folded Above The ChinEach image has two label files: one in PascalVOC format and one in YOLO format. These files contain the bounding boxes of the face of the person, the mask or the respirator, and both.The database is ready-to-be-used and, due to its size, it was divided into two large files: WWMR-DB - Part 1.zip and WWMR-DB - Part 2.zip.The labels are contained in WWMR-DB - Labels.zip file, while Labels assigned to the images.txt file contains information about the labels assigned to each image, which identify the subject number, the depicted class, type of mask or respirator worn and the subject's face rotation.Due to the limitations imposed by the coronavirus, the database was created by asking volunteers for selfies through Google Forms. For this reason:- number of images per class- image quality- intra-class differences- rotation of the facecould also have great variations.

《佩戴口罩或呼吸器的佩戴方式数据库》(WWMR-DB)系一测试型数据库,旨在对现有口罩检测系统的行为进行对比分析,其以最接近实际案例的图像为基准。该数据库包含1222张图片,划分为8个类别,展示了口罩或呼吸器最常见的佩戴方式,具体包括:未佩戴口罩或呼吸器、正确佩戴口罩或呼吸器、口罩或呼吸器置于鼻下、口罩或呼吸器置于下巴下、口罩或呼吸器悬挂于耳部、口罩或呼吸器置于鼻尖、口罩或呼吸器置于额头、口罩折叠于下巴上方。每张图片均附有两个标签文件:一个为PascalVOC格式,另一个为YOLO格式,这两者均包含人脸、口罩或呼吸器的边界框以及两者的信息。该数据库已准备就绪,鉴于其规模庞大,故分为两个大型文件:WWMR-DB - 第1部分.zip和WWMR-DB - 第2部分.zip。标签文件包含于WWMR-DB - 标签.zip文件中,而分配给图像的标签信息.txt文件则包含了关于每张图像分配的标签信息,这些信息包括主题编号、描绘的类别、所佩戴口罩或呼吸器的类型以及主题人脸的旋转角度。鉴于冠状病毒带来的限制,该数据库通过Google Forms征求志愿者自拍照片的方式创建。因此,每个类别中的图像数量、图像质量、类别内差异以及人脸旋转等方面都可能存在较大变化。
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