## 5 Review of Mask-Face Detection Hardware Systems
### Recent Mask-Face Detection Systems
#### 5.1.1 Hardware Analysis  
[PERSON] et al. [75] provided an elaborate comparison among multiple hardware choices. They set a desktop-based implementation of an Intel I7-8650U CPU 1.9 GHz, and 16 GB RAM as the baseline. Then three embedded hardware systems were designed to deploy the deep learning detection model. From the software level, the authors proposed a customized CNN model named _MaskDetect_. Compared to well-known pretrained models such as VGG-16 [44] and ResNet-50V2 [71], _MaskDetect_ was light-weight, thus saving memory and accelerating the inference to a great extent. Furthermore, to fit the specific hardware accelerator, the author provided three different quantization strategies. A memory reduction from the model size of 11.5 MB to 0.983 MB can be realized under the 32-bit floats to 8-bit integer quantization compression. As a result, three systems, namely the a.Raspberry Pi 4 computational platform + Google Coral Edge TPU + 8-bit quantized model, b.Raspberry Pi 4 computational platform + Intel Neural Compute Stick 2 (INCS2) + Compressed model in float16 precision and c.Jetson Nano + 128 Maxwell cores + Compressed model in float32 precision achieved the following performances: FPS--19, accuracy--90.4%; FPS--18, accuracy--94.3% and FPS--22, accuracy--94.2%, respectively. The NVIDIA Jetson Nano is least costly in terms of design and had the best performance of acceleration while its detection accuracy was comparable. This paper is the only research in which multiple systems were designed and an in-depth comparison was made from different angles.  
In [94], the authors provided a similar result to [75] that the NVIDIA Jetson Nano might be a more suitable platform for developing mask-face detection systems. They deployed various deep learning models of both image classification and object detection on Jetson Nano and Raspberry Pi3 for mask-face detection and performed a real-time analysis. Consequently, the Jetson Nano outperformed Raspberry Pi3 by a substantial degree. As an example, Jetson Nano provided 36 FPS with the well-known ResNet50 [28] while the FPS was 1.4 for Raspberry Pi3 in the same configuration. As a result, though most systems tend to deploy models on Raspberry Pi embedded systems, the above research revealed that Raspberry Pi might not be the most suitable choice for designing mask-face detection systems. More hardware devices should be explored.  
Besides looking for suitable hardware devices, [PERSON] et al. [76] achieved great progress in addition to designing detection models. In their approach, they used the multithreading technique to avoid the model delay. They trained Haar Cascade Classifiers (HCC) [78] to detect mask-wearing and faces without a mask. They used two HCCs to detect faces and months, respectively. The mask-face detection results were based on a combination of two outcomes. While no deep learning models were deployed, it was proved that the traditional machine learning model could cause significant delay due to the algorithm's running time. To avoid such delays, they incorporated multithreading whereby they pipelined the video frame through the FPGA unit and executed the detection model in the ARM core. Therefore, the algorithm delay could be replaced by a buffer delay which improved the FPS to a large extent.