探测器
计算机科学
面子(社会学概念)
人工智能
编码(集合论)
领域(数学)
计算机视觉
人脸检测
功能(生物学)
模式识别(心理学)
面部识别系统
数学
集合(抽象数据类型)
电信
社会科学
进化生物学
生物
社会学
程序设计语言
纯数学
作者
Yu Zhou,Haihong Huang,Weijun Chen,Yongxin Su,Yahui Liu,Xiuying Wang
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:3
标识
DOI:10.48550/arxiv.2208.02019
摘要
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
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