COVID-19疫情期间实时医用口罩检测的目标检测模型与优化方法综述及实现
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic
AI总结：
本研究评估目标检测模型的速度/精度权衡，基于YOLOv5s提出优化模型，利用迁移学习实现实时医用口罩检测，速度达69帧/秒，精度保持67%，超越SE-YOLOv3。
摘要：
卷积神经网络（CNN）因其较高的准确性而常用于目标检测问题。然而，当考虑检测速度时，基于CNN的检测模型的性能表现并不明确。据我们所知，现有文献中关于速度/准确性权衡的可用方法评估尚不充分。本研究在Common Objects in Context（COCO）数据集上评估了最基础的目标检测模型，关注其速度/准确性权衡、内存消耗以及计算和存储成本。接着，我们选择了一种高效模型YOLOv5，用于训练一个针对性的、尚未探索的人脸佩戴医用口罩数据集——Properly-Wearing Masked Faces Dataset（PWMFD），并分析了特定优化技术对实时医用口罩检测的益处：迁移学习、数据增强和Squeeze-and-Excitation注意力机制。结合我们在COVID-19疫情背景下的发现，我们提出了一种基于YOLOv5s的优化模型，利用迁移学习来检测正确和错误佩戴的医用口罩，该模型在PWMFD数据集上的速度（每秒69帧）超过最先进的SE-YOLOv3模型两倍以上，同时保持了相同的平均精度（67%）。
英文摘要：
Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%).
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