# ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment
## 1 Introduction  
Since the outbreak of COVID-19 in 2019, the lives and health of people worldwide have been greatly threatened, causing long-term disruption to people's lives and work, and seriously hampering global economic development. The causative pathogen is a novel coronavirus (SARS-CoV-2), characterized by rapid transmission and high adaptability. Viruses are generally transmitted via droplets, contact, and aerosols, with droplet transmission being their main mode of transmission. If a patient sneezes, speaks, or spits, droplets carrying the novel coronavirus can be ejected, causing the virus to spread rapidly. Thus far, the virus has differentiated into Alpha, Beta, Gamma, Delta, and other variant strains [1]. As of 2022, 600 million people have been diagnosed cumulatively worldwide, with an average of one death from COVID-19 every 8 s [2]. Moreover, the rate of COVID-19 infection is growing exponentially worldwide, with an average daily growth rate of 1.9 times. Despite the increasing number of people being vaccinated, COVID-19 still greatly threatens people's health and safety, and the importance of protection against it cannot be underestimated [3]. As the main centers of epidemic prevention and control, hospitals, isolation hotels, and nucleic acid testing sites are crowded. As the core of COVID-19 prevention and control, the health and safety of medical and nursing personnel are extremely important, and once cross-infection occurs, it can have an incalculable impact on the prevention and control of the epidemic. Theoretically, medical personal protective equipment can isolate viruses, harmful ultra-fine dust, etc., thereby effectively preventing the spread of viruses. Therefore,medical and nursing personnel needs to wear medical personal protective equipment such as protective suits, goggles, and masks [4]. However, due to a lack of protective awareness and insufficient supply and demand of medical personal protective equipment, many medical and nursing personnel are not required to wear medical personal protective equipment, resulting in high levels of infection. Currently, the supervision of wearing medical personal protective equipment is mainly through manual testing, which results in waste of human resources and the problem of missed and wrong detections. Therefore, with the development of computer vision (CV), automatic identification and detection of medical personal protective equipment based on deep learning are of great importance. Traditional image detection algorithms are based on the shape and color of objects for recognition [5]. These complex algorithms have certain limitations to their application, such as a lack of sufficient robustness and error detection [6]. Deep-learning-based object detection algorithms have become popular for current research and applications, as they can overcome the limitations of traditional image detection algorithms and effectively extract object features from complex scenes. Generally, deep-learning-based object detection approaches can be divided into two categories: The first is two-stage algorithms, which are based on candidate regions, such as Fast RCNN (region-based convolutional neural network) [7] and Faster RCNN [8]. Despite the high detection accuracy of two-stage algorithms, it is difficult to increase their detection speed effectively due to the amount of computation required for extracting candidate regions. The other category is one-stage algorithms, which are end-to-end learning algorithms with high detection speed, such as SSD (single-shot multi-box detector) [9], RetinaNet [10], and YOLO (You Only Look Once) series algorithms [11; 12; 13; 14; 15; 16; 17]. These algorithms directly generate the class probability and position coordinate values of the object, obtaining the final detection result from a single inspection.