Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction

The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the miss...

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Autores principales:	Qingqing Xu, Zhiyu Zhu, Huilin Ge, Zheqing Zhang, Xu Zang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/76840162c77d40828881510030607b64
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spelling	oai:doaj.org-article:76840162c77d40828881510030607b642021-11-29T00:56:23ZEffective Face Detector Based on YOLOv5 and Superresolution Reconstruction1748-671810.1155/2021/7748350https://doaj.org/article/76840162c77d40828881510030607b642021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7748350https://doaj.org/toc/1748-6718The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the missing and false detection of small faces, due to image quality, face scale, light, and other factors. In this paper, a two-level face detection model called SR-YOLOv5 is proposed to address some problems of dense small faces in actual scenarios. The research first optimized the backbone and loss function of YOLOv5, which is aimed at achieving better performance in terms of mean average precision (mAP) and speed. Then, to improve face detection in blurred scenes or low-resolution situations, we integrated image superresolution technology on the detection head. In addition, some representative deep-learning algorithm based on face detection is discussed by grouping them into a few major categories, and the popular face detection benchmarks are enumerated in detail. Finally, the wider face dataset is used to train and test the SR-YOLOv5 model. Compared with multitask convolutional neural network (MTCNN), Contextual Multi-Scale Region-based CNN (CMS-RCNN), Finding Tiny Faces (HR), Single Shot Scale-invariant Face Detector (S3FD), and TinaFace algorithms, it is verified that the proposed model has higher detection precision, which is 0.7%, 0.6%, and 2.9% higher than the top one. SR-YOLOv5 can effectively use face information to accurately detect hard-to-detect face targets in complex scenes.Qingqing XuZhiyu ZhuHuilin GeZheqing ZhangXu ZangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Qingqing Xu Zhiyu Zhu Huilin Ge Zheqing Zhang Xu Zang Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
description	The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the missing and false detection of small faces, due to image quality, face scale, light, and other factors. In this paper, a two-level face detection model called SR-YOLOv5 is proposed to address some problems of dense small faces in actual scenarios. The research first optimized the backbone and loss function of YOLOv5, which is aimed at achieving better performance in terms of mean average precision (mAP) and speed. Then, to improve face detection in blurred scenes or low-resolution situations, we integrated image superresolution technology on the detection head. In addition, some representative deep-learning algorithm based on face detection is discussed by grouping them into a few major categories, and the popular face detection benchmarks are enumerated in detail. Finally, the wider face dataset is used to train and test the SR-YOLOv5 model. Compared with multitask convolutional neural network (MTCNN), Contextual Multi-Scale Region-based CNN (CMS-RCNN), Finding Tiny Faces (HR), Single Shot Scale-invariant Face Detector (S3FD), and TinaFace algorithms, it is verified that the proposed model has higher detection precision, which is 0.7%, 0.6%, and 2.9% higher than the top one. SR-YOLOv5 can effectively use face information to accurately detect hard-to-detect face targets in complex scenes.
format	article
author	Qingqing Xu Zhiyu Zhu Huilin Ge Zheqing Zhang Xu Zang
author_facet	Qingqing Xu Zhiyu Zhu Huilin Ge Zheqing Zhang Xu Zang
author_sort	Qingqing Xu
title	Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
title_short	Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
title_full	Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
title_fullStr	Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
title_full_unstemmed	Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction
title_sort	effective face detector based on yolov5 and superresolution reconstruction
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/76840162c77d40828881510030607b64
work_keys_str_mv	AT qingqingxu effectivefacedetectorbasedonyolov5andsuperresolutionreconstruction AT zhiyuzhu effectivefacedetectorbasedonyolov5andsuperresolutionreconstruction AT huilinge effectivefacedetectorbasedonyolov5andsuperresolutionreconstruction AT zheqingzhang effectivefacedetectorbasedonyolov5andsuperresolutionreconstruction AT xuzang effectivefacedetectorbasedonyolov5andsuperresolutionreconstruction
_version_	1718407715334651904

Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction

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