[1]赵文清,杨盼盼.双向特征融合与注意力机制结合的目标检测[J].智能系统学报,2021,16(6):1098-1105.[doi:10.11992/tis.202012029]
 ZHAO Wenqing,YANG Panpan.Target detection based on bidirectional feature fusion and an attention mechanism[J].CAAI Transactions on Intelligent Systems,2021,16(6):1098-1105.[doi:10.11992/tis.202012029]
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双向特征融合与注意力机制结合的目标检测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年6期
页码:
1098-1105
栏目:
学术论文—知识工程
出版日期:
2021-11-05

文章信息/Info

Title:
Target detection based on bidirectional feature fusion and an attention mechanism
作者:
赵文清12 杨盼盼1
1. 华北电力大学 控制与计算机工程学院,河北 保定 071003;
2. 复杂能源系统智能计算教育部工程研究中心,河北 保定 071003
Author(s):
ZHAO Wenqing12 YANG Panpan1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of the Ministry of education for Intelligent Computing of Complex Energy System, Baoding 071003, China
关键词:
特征金字塔双向融合特征提取SeNet注意力机制样本语义信息目标检测深度学习
Keywords:
feature pyramidbidirectional fusionfeature extractionSeNet attention mechanismsamplesemantic informationtarget detectiondeep learning
分类号:
TP391
DOI:
10.11992/tis.202012029
摘要:
目标检测使用特征金字塔检测不同尺度的物体时,忽略了高层信息和低层信息之间的关系,导致检测效果差;此外,针对某些尺度的目标,检测中容易出现漏检。本文提出双向特征融合与注意力机制结合的方法进行目标检测。首先,对SSD(single shot multibox detector)模型深层特征层与浅层特征层进行特征融合,然后将得到的特征与深层特征层进行融合。其次,在双向融合中加入了通道注意力机制,增强了语义信息。最后,提出了一种改进的正负样本判定策略,降低目标的漏检率。将本文提出的算法与当前主流算法在VOC数据集上进行了比较,结果表明,本文提出的算法在对目标进行检测时,目标平均准确率有较大提高。
Abstract:
When using a feature pyramid to detect objects of different dimensions, the relationship between high- and low-level information is ignored, resulting in a poor detection effect; in addition, for targets of a certain scale, detection is easily missed. In this paper, a method combining bidirectional feature fusion and an attention mechanism is proposed for target detection. First, the deep and shallow feature layers of the single-shot multibox detector (SSD) model are fused, then the obtained features are fused with the deep feature layer. Second, the channel attention mechanism is added to the two-way fusion to enhance semantic information. Finally, an improved positive and negative sample decision strategy is proposed to reduce the target misdetection rate. The algorithm proposed in this paper is compared with the current mainstream algorithms in the VOC dataset. The results show that the average accuracy of the proposed algorithm is greatly improved when detecting targets.

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相似文献/References:

[1]赵文清,孔子旭,赵振兵.隔级融合特征金字塔与CornerNet相结合的小目标检测[J].智能系统学报,2021,16(1):108.[doi:10.11992/tis.202004033]
 ZHAO Wenqing,KONG Zixu,ZHAO Zhenbing.Small target detection based on a combination of feature pyramid and CornerNet[J].CAAI Transactions on Intelligent Systems,2021,16(6):108.[doi:10.11992/tis.202004033]

备注/Memo

备注/Memo:
收稿日期:2020-12-17。
基金项目:河北省自然科学基金项目(F2021502013);中央高校基本科研业务费面上项目(2020MS153,2021PT018)
作者简介:赵文清,教授,博士,主要研究方向为人工智能与图像处理。获河北省科技进步二等奖、三等奖各1项。发表学术论文50余篇;杨盼盼,硕士研究生,主要研究方向为深度学习和目标检测
通讯作者:赵文清.E-mail:zhaowenqing@ncepu.edu.cn
更新日期/Last Update: 2021-12-25