# ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment
## 2 Related Work  
### Existing Work  
Most current medical personal protective equipment detection methods focus only on identifying masks. [PERSON] et al. [18] used three datasets: Labeled Faces in the Wild (LFW), Simulated Masked Face Dataset (SMFD), and Real-World Masked Face Dataset (RMFD). They also introduced a hybrid model consisting of two parts: ResNet-50 for feature extraction and traditional machine learning algorithms for classifying whether or not masks are worn. The experimental results demonstrate that the model introduced in this paper is better than other machine learning algorithms. However, they tested their model on simulated mask datasets--not real-world mask images--and the machine learning algorithms had very poor generalization capabilities. [PERSON] et al. [19] proposed a new algorithm for mask classification and detection fusing Efficient-YOLOv3 and transfer learning, with good results, but there was still room for improvement in terms of detection speed. [PERSON] et al. [20] proposed the SSDMNV2 model for face mask detection; in this model, the authors used the SSD algorithm to detect faces in real time, using the pre-trained model MobileNetV2 to predict whether the people were wearing masks or not. [PERSON] et al. [21] improved the YOLOv4 algorithm to achieve better results in mask recognition and standard wear detection.  
To date, few studies have been conducted for the detection of gloves, goggles, protective suits, face shields, and gloves. [PERSON] et al. [22] designed a surface defect detection system for medical gloves; it uses a dual-model detection strategy, which divides edge detection and texture detection into two steps. The experimental results demonstrated that the system has a false detection rate of less than 0.05% and a missed detection rate of less than 2%. However, the detection strategy requires high computational costs and can only detect medical gloves. [PERSON] et al. [23] designed a ski goggle defect detection and classification system. Although this system has high accuracy in defect detection and classification, it can only be applied to ski goggles. To perform multi-class personal protective equipment (PPE) compliance detection, [PERSON] et al. [24] proposed an extensible pose-guided anchoring framework. They then used a shallow CNN-based classifier to identify PPE. Although their proposed strategy has a higher detection accuracy, their detection categories are helmets and reflective clothing; hence, this approach cannot be used directly for the detection of medical personal protective equipment.  
None of the aforementioned detection categories apply to the detection of medical personal protective equipment, and the detection models cannot balance detection speed and detection accuracy well. Therefore, in order to handle these issues, we used ME-YOLO-based medical personal protective equipment detection to detect five types of medical equipment: suits, face shields, gloves, goggles, and masks. In addition, our proposed ME-YOLO algorithm can better balance performance and efficiency, enabling it to meet the requirements of real-time detection.