改进YOLOV5s的多尺度融合口罩佩戴检测方法

doi:10.3778/j.issn.1002-8331.2301-0171

摘要/Abstract

摘要： 针对复杂场景下口罩佩戴检测算法存在小目标检测精度低和漏检率高的问题，提出一种基于改进YOLOV5s的口罩佩戴检测模型。融合倒置自适应注意力模块IAAM（inverted adaptive attention module），缓解连续下采样导致的信息丢失问题，同时增强网络的特征融合能力；为了平衡多尺度检测层接收到的有效信息量，设计了通道特征分组模块CFGM（channel feature grouping module），提高了小目标检测精度；结合实际场景中的数据特征，使用EIoU Loss损失函数并取消数据增强中的色彩空间变换。实验结果表明，改进后的模型在检测精度、推理速度和小目标检测能力等方面均有提升，能够完成复杂场景下实时口罩佩戴检测任务。

关键词: 口罩佩戴检测, YOLOV5s, 小目标检测, 注意力机制, 特征分组, EIoU损失

Abstract: Aiming at the problems of low detection accuracy and high missed detection rate of small targets in the mask wearing detection algorithm in complex scenes, a mask wearing detection model based on improved YOLOV5s is proposed. In order to alleviate the problem of information loss caused by continuous downsampling, an inverted adaptive attention module（IAAM） is proposed. In order to improve the sensing ability of high-resolution detection layer to global information, a channel feature grouping module（CFGM） is designed, and the small object detection accuracy is greatly improved by using this module. Combined with the data features in the actual scene, EIoU Loss is introduced, and the color space transformation in data enhancement is cancelled. Experimental results show that the improved model has improved recall rate, detection accuracy, inference speed and detection ability of small targets, and can complete real-time mask wearing detection task in complex scenes.

Key words: mask wearing detection, YOLOV5s, small object detection, attention mechanism, feature grouping, EIoU loss

杨国亮, 余帅英, 杨浩. 改进YOLOV5s的多尺度融合口罩佩戴检测方法[J]. 计算机工程与应用, 2023, 59(14): 184-191.

YANG Guoliang, YU Shuaiying, YANG Hao. Multi-Scale Fusion Mask Wearing Detection Method Based on Improved YOLOV5s[J]. Computer Engineering and Applications, 2023, 59(14): 184-191.

参考文献

[1] HU Z L，SONG C，XU C J，et al.Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing，China[J].Science China（Life Sciences），2020，63（5）：706-719.
[2] 吴帅，徐勇，赵东宁.基于深度卷积网络的目标检测综述[J].模式识别与人工智能，2018，31（4）：335-346.
WU S，XU Y，ZHAO D N.Survey of object detection based on deep convolutional network[J].Pattern Recognition and Artificial Intelligence，2018，31（4）：335-346.
[3] ZAIDI S S A，ANSARI M S，ASLAM A，et al.A survey of modern deep learning based object detection models[J].Digital Signal Processing，2022，126：103514.
[4] DESAI K，PARIKH S，PATEL K，et al.Survey of object detection algorithms and techniques[C]//Cybernetics，Cognition and Machine Learning Applications，2020：247-257.
[5] 李小波，李阳贵，郭宁，等.融合注意力机制的YOLOv5口罩检测算法[J].图学学报，2023，44（1）：16-25.
LI X B，LI Y G，GUO N，et al.Mask detection algorithm based on YOLOv5 intergrating attention mechanism[J].Journal of Graphics，2023，44（1）：16-25.
[6] 陈昭俊，储珺，曾伦杰.基于动态加权类别平衡损失的多类别口罩佩戴检测[J].图学学报，2022，43（4）：590-598.
CHEN Z J，CHU J，ZENG L J.Multi category mask wearing detection based on dynamic weighted category balance loss[J].Journal of Graphics，2022，43（4）：590-598.
[7] 王兵，乐红霞，李文璟，等.改进YOLO轻量化网络的口罩检测算法[J].计算机工程与应用，2021，57（8）：62-69.
WANG B，LE H X，LI W J，et al.Mask detection algorithm based on improved YOLO lightweight network[J].Computer Engineering and Applications，2021，57（8）：62-69.
[8] 程浩然，王薪陶，李俊燃，等.改进YOLOv4-tiny的疫情协同口罩佩戴检测方法[J].计算机工程与应用：1-16（2022-08-12）[2022-12-19].http：//kns.cnki.net/kcms/detail/11.2127.TP.20220812.1448.010.html.
CHENG H R，WANG X T，LI J R，et al.Improved YOLOv4-tiny epidemic collaborative mask wearing detection method[J].Computer Engineering and Applications：1-16（2022-08-12）[2022-12-19].http：//kns.cnki.net/kcms/detail/11.2127.TP.
20220812.1448.010.html.
[9] 朱杰，王建立，王斌.基于YOLOv4-tiny改进的轻量级口罩检测算法[J].液晶与显示，2021，36（11）：1525-1534.
ZHU J，WANG J L，WANG B.Lightweight mask detection algorithm based on improved YOLOv4-tiny[J].Chinese Journal Liquid Crystal and Display，2021，36（11）：1525-1534.
[10] 卢云聪.基于SSD算法的口罩佩戴检测模型[J].科技与创新，2022（18）：101-103.
LU Y C.Mask wearing detection model based on SSD algorithm[J].Technology and Innovation，2022（18）：101-103.
[11] 付莲莲，丁鑫圣，王映龙.基于帧间差分法的口罩佩戴检测算法研究[J].计算机仿真，2022，39（10）：250-254.
FU L L，DING X S，WANG Y L.Research on mask wearing detection algorithm based on frame difference method[J].Computer Simulation，2022，39（10）：250-254.
[12] WANG J，CHEN Y，GAO M，et al.Improved YOLOv5 network for real-time multi-scale traffic signdetection[J].arXiv：2112.08782，2021.
[13] SHI W，CABALLERO J，HUSZáR F，et al.Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1874-1883.
[14] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[15] JIN Z，YU D，SONG L，et al.You should look at all objects[C]//European Conference on Computer Vision.Cham：Springer，2022：332-349.
[16] LIU S，HUANG D，WANG Y.Learning spatial fusion for single-shot object detection[J].arXiv：1911.09516，2019.
[17] ZHANG Y F，REN W，ZHANG Z，et al.Focal and efficient IOU loss for accurate bounding box regression[J].Neurocomputing，2022，506：146-157.
[18] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2980-2988.
[19] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//European Conference on Computer Vision.Cham：Springer，2016：21-37.
[20] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015.
[21] TIAN Z，SHEN C，CHEN H，et al.FCOS：fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：9627-9636.