Face masks have long been used in many areas of everyday life to protect against the inhalation of hazardous fumes and particles. They also offer an effective solution in healthcare for bi-directional protection against air-borne diseases. Wearing and positioning the mask correctly is essential for its function. Convolutional neural networks (CNNs) offer an excellent solution for face recognition and classification of correct mask wearing and positioning. In the context of the ongoing COVID-19 pandemic, such algorithms can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus. These application scenarios impose major challenges to the underlying compute platform. The inference hardware must be cheap, small and energy efficient, while providing sufficient memory and compute power to execute accurate CNNs at a reasonably low latency. To maintain data privacy of the public, all processing must remain on the edge-device, without any communication with cloud servers. To address these challenges, we present a low-power binary neural network classifier for correct facial-mask wear and positioning. The classification task is implemented on an embedded FPGA, performing high-throughput binary operations. Classification can take place at up to ~6400 frames-per-second, easily enabling multi-camera, speed-gate settings or statistics collection in crowd settings. When deployed on a single entrance or gate, the idle power consumption is reduced to 1.6W, improving the battery-life of the device. We achieve an accuracy of up to 98% for four wearing positions of the MaskedFace-Net dataset. To maintain equivalent classification accuracy for all face structures, skin-tones, hair types, and mask types, the algorithms are tested for their ability to generalize the relevant features over all subjects using the Grad-CAM approach.
翻译:长期以来,在日常生活的许多领域都使用面罩来防止吸入有害烟雾和颗粒,这些算法也为防止吸入有害烟雾和颗粒提供了有效的解决方案,为双向网络疾病提供双向保护保护平台。正确穿戴和定位面具对其功能至关重要。穿戴和定位面具对正确功能至关重要。 革命神经网络(CNNs)为识别和分类正确的面具穿戴和定位提供了一个极好的解决方案。 在目前COVID-19大流行的情况下,这种算法可用于公司大楼、机场、购物区和其他室内地点的入口处,以缓解病毒的传播。这些应用情景给基础的计算机化平台带来了重大挑战。 推断硬件必须廉价、小型、节能高效,同时提供足够的记忆力和计算能力,以便在相当低的延迟时间段执行准确的CNN。 为了维护公众的数据隐私,所有处理都必须留在边缘平台上,不与云层服务器有任何沟通。 为了应对这些挑战,我们展示一个低功率的双向网络分类,用于纠正面图像的磨损和定位。这些应用的分类任务是在一个等值的面结构上执行一个直径直径直径直径的直径直径直径直径直径直径直径直径直置的直径直径直径直径直径直径直径直置的直置的直径直置的直径直置的直置的直径直径直径直径直置的直径直径直置的直径直径直径直径直径直置的直置的直置的直置的直径直置的直置的直置的直置的直径径径径径径直径方位, 。