摘要
针对牛脸检测时,存在的检测精度不高、牛脸较小被漏检或误检等问题,提出一种改进的Mask R-CNN+MResNet模型。首先,在ResNet101网络的基础上提出一种MResNet网络,通过对ResNet101网络的改进,提高了模型检测精度。其次,对模型的RPN网络的锚框尺寸进行调整,提高了模型对较小目标的牛脸检测能力。实验结果表明,MResNet网络对牛脸检测精度相比较原始的网络模型,提高了12.6%;改进后的模型对于小目标检测能力平均精度较原始模型提高了2.4%。说明该模型能有效的实现小目标牛脸的检测,具有实际应用价值。
To address issues such as low detection accuracy and the occurrence of missing or misidentifying bovine faces due to their small size,we propose an enhanced model called Mask R-CNN+MResNet.Firstly,we introduce a MResNet network based on the ResNet101 architecture,which enhances the detection accuracy of the model by improving upon ResNet101.Secondly,we adjust the anchor frame size of the model's RPN network to enhance its capability in detecting small targets.Experimental results demonstrate that compared to the original network model,MResNet achieves a 12.6%improvement in bovine face detection accuracy.Furthermore,the improved model exhibits a 2.4%increase in average accuracy for detecting small targets,compared to the original model.These results indicate that this model effectively detects small target cow faces and holds practical application value.
作者
关忠榜
杨颜博
李敏超
Guan Zhongbang;Yang Yanbo;Li Minchao(College of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)
出处
《电子测量技术》
北大核心
2023年第24期133-138,共6页
Electronic Measurement Technology
基金
内蒙古自治区自然科学基金委联合项目(2020LH06006)
内蒙古自治区科技厅(2019ZD025)
内蒙古古教育厅(0406082219)
内蒙古包头市昆区科技局科技计划(YF2021011)
内蒙古自然科学基金(2021MS06007)
科技兴蒙行动重点专项(XM2021BT12)资助
作者简介
关忠榜,硕士研究生,主要研究方向为牛脸的目标检测。E-mail:gzb_6593@126.com;通信作者:杨颜博,博士,讲师,主要研究方向为大数据、人工智能算法的理论与应用研究、网络编码理论与应用研究。E-mail:yangyanbo@imust.edu.cn;李敏超,博士,讲师,主要研究方向为阵列信号处理,雷达探测与成像和人工智能方向。
