ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment

Abstract

Corona Virus Disease 2019 (COVID-19) poses a significant threat to human health and safety. As the core of the prevention and control of COVID-19, the health and safety of medical and nursing personnel are extremely important, and the standardized use of medical personal protective equipment can effectively prevent cross-infection. Due to the existence of severe occlusion and overlap, traditional image processing methods struggle to meet the demand for real-time detection. To address these problems, we propose the ME-YOLO model, which is an improved model based on the one-stage detector. To improve the feature extraction ability of the backbone network, we propose a feature fusion module (FFM) merged with the C3 module, named C3_FFM. To fully retain the semantic information and global features of the up-sampled feature map, we propose an up-sampling enhancement module (USEM). Furthermore, to achieve high-accuracy localization, we use EIoU as the loss function of the border regression. The experimental results demonstrate that ME-YOLO can better balance performance (97.2% mAP) and efficiency (53 FPS), meeting the requirements of real-time detection.