IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

Object detection is a fundamental task in computer vision that aims to locate and classify objects in images or videos. The one-stage You Only Look Once (YOLO) models are popular approaches to object detection. Real-time monitoring of mask wearing is necessary, especially for preventing the spread of the COVID-19 virus. While YOLO detectors facing challenges include improving the robustness of object detectors against occlusion, scale variation, handling false detection and false negative, and maintaining the balance between higher precision detection and faster inference time. In this study, a novel object detection model called Improved You Only Look Once and None Left (IYOLO-NL) based on YOLOv5 was proposed for real-time mask wearing detection. To fulfill the requirement of real-time detection, the lightweight IYOLO-NL was developed by using novel CSPNet-Ghost and SSPP bottleneck architecture. To prevent any missed correct results, IYOLO-NL integrates the proposed PANet-SC with a multi-level prediction scheme. To achieve high precision and handle sample allocation properly, the proposed global dynamic-k label assignment strategy was utilized in an anchor-free manner. A large dataset of face masks (FMD) was created, consisting of 6130 images, for use in conducting experiments on IYOLO-NL and other models. The experiment results show that IYOLO-NL surpasses other state-of-the-art (SOTA) methods and achieves 98.8% accuracy while maintaining 130 FPS.