YOLOv5-CCE:一种基于CA和EIoU的目标检测算法
YOLOv5-CCE:an Object Detection Algorithm Based on CA and EIoU
王军 1黄博文 1蔡景贵1
作者信息
- 1. 沈阳化工大学计算机科学与技术学院,沈阳 110142;辽宁省化工过程工业智能化技术重点实验室,沈阳 110142
- 折叠
摘要
为了减少YOLOv5模型在复杂环境下的误检率和漏检率,提出一种基于CA(Coordinate Attention)和EIoU(Efficient Intersection over Union)的目标检测模型YOLOv5-CCE.首先向Neck网络中的部分C3_2模块中嵌入坐标注意力机制CA,增强模型对特征的提取能力;其次为提高回归精度,提出一种基于Focal EIoU Loss改进的Focal CEIoU Loss.实验结果表明,在PASCAL VOC 2007+2012数据集上,YOLOv5-CCE模型在参数量和计算量基本保持不变的情况下,相较于原模型mAP@0.5、mAP@0.5:0.95和准确率分别提升了1.4%、1.3%和3.7%,因此,YOLOv5-CCE模型可以更好地适应复杂环境下的目标检测任务.
Abstract
In order to reduce the false detection rate and missed detection rate of the YOLOv5 model in complex environments,a target detection model YOLOv5-CCE based on CA(Coordinate Attention)and EIoU(Efficient Intersection over Union)is proposed.Firstly,the coordinate attention mechanism CA is embedded in partial C3_2 module in the Neck network to enhance the feature extraction ability of the model;secondly,to improve the regression accuracy,an improved Focal CEIoU Loss based on Focal EIoU Loss is proposed.The experimental results show that on the PASCAL VOC 2007+2012 data set,the YOLOv5-CCE model maintains the parameters and calculations basically unchanged,compared with the original model of mAP@0.5 and mAP@0.5:0.95 and the accuracy rate respectively,its accuracy rate has increased by 1.4%,1.3%and 3.7%respectively.Therefore,the YOLOv5-CCE model can better adapt to the target detection task in complex envi-ronments.
关键词
YOLOv5算法/EIoU/Focal/Loss/CA注意力机制/目标检测Key words
YOLOv5 algorithm/EIoU/Focal Loss/CA attention mechanism/object detection引用本文复制引用
基金项目
辽宁省自然科学基金(2022-MS-291)
辽宁省教育厅科研基金(LJ2020024)
中国高校产学研创新基金(2021LD06009)
辽宁省教育厅科研基金资助项目(LJKMZ20220781)
出版年
2024
国家科技期刊平台
NSTL
万方数据