In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV2

翻译：近年来,基于深层学习的面对面检测算法取得了巨大进步。这些算法一般可以分为两类,即两阶段探测器,如快速R-CNN和一阶段探测器,如YOLO。由于精度和速度之间的平衡更加平衡,在许多应用中广泛使用了一阶段探测器。此外,在本文中,我们提议基于一阶段探测器YOLOv5的实时面对面检测器,名为YOLO-FaceV2。我们设计了一个称为RFE的受视场增强模块,以强化小脸部的可接收场,并使用NWD Loss来弥补IOU对小对象位置偏差的敏感度。关于面闭路面,我们提出了一个名为SEAM的注意模块,并引入了反向损失来解决这个问题。此外,我们用重量函数幻灯片来解决简单和硬样品之间的不平衡,并使用有效的可接受字段的信息来设计锚。大Face数据集的实验结果显示,我们的脸探测器超越了YOLOO及其变体的变体,可以在所有易、中和硬子FOVS/FALSOLSU中找到。