特征金字塔多尺度全卷积目标检测算法

doi:10.3785/j.issn.1008-973X.2019.03.014

浙江大学学报(工学版)

2019, Vol. 53

Issue (3): 533-540 DOI: 10.3785/j.issn.1008-973X.2019.03.014

计算机技术

特征金字塔多尺度全卷积目标检测算法

林志洁1,2(

),罗壮2,赵磊2,*(

),鲁东明2

1. 浙江科技学院信息与电子工程学院，浙江杭州 310023
2. 浙江大学计算机学院，浙江杭州 310027

Multi-scale convolution target detection algorithm with feature pyramid

Zhi-jie LIN1,2(

),Zhuang LUO2,Lei ZHAO2,*(

),Dong-ming LU2

1. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2. School of computer science, Zhejiang University, Hangzhou 310027, China

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摘要：

基于区域建议网络构建一种特征金字塔多尺度网络结构，并结合全卷积操作完成微小目标与类别无关目标的检测. 为了提升图像中微小目标的检测精度，构建基于侧链接融合的3层金字塔结构网络，充分利用语义级别比较低的图像卷积特征. 为了提高类别无关的图像目标检测鲁棒性，提出特定的非极大值抑制算法，在重叠目标过滤时消除冗余目标窗口，并对目标窗口进行位置精修. 在PASCAL VOC 2007、PASCAL VOC 2012以及古代绘画数据集上的实验结果表明：所提算法对于微小目标、多尺度目标检测及种类无关的目标检测的检测精度高于已有算法.

关键词： 图像目标检测; 图像特征金字塔; 多尺度全卷积; 微小目标检测; 类别无关目标检测

Abstract:

A feature pyramid multi-scale network structure was constructed based on the region recommendation network, the small target and class-independent image target were detected by combining the full convolution operation. In order to improve the detection accuracy of small targets in images, a three-layer pyramid structure network based on side link fusion was constructed, which made full use of the convolution features of images with low semantic level. To improve the robustness of class-independent image target detection, a specific non-maximum suppression algorithm was proposed to eliminate redundant target windows in overlapping target filtering and to refine the location of the target windows. The experimental results on PASCAL VOC 2007, PASCAL VOC 2012 and ancient painting datasets show that the detection accuracy of the proposed algorithm for small targets, multi-scale targets and type-independent targets is higher than that of the existing algorithms.

Key words: image target detection image feature pyramid multi-scale full convolution small targets detection category-independent target detection

收稿日期: 2018-04-27 出版日期: 2019-03-04

CLC:

TP 391

通讯作者: 赵磊 E-mail: bytelin@qq.com;cszhl@zju.edu.cn

作者简介: 林志洁(1980?)，男，博士，从事深度学习、图像处理研究. orcid.org/0000-0002-8605-2834. E-mail: bytelin@qq.com

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引用本文:

林志洁,罗壮,赵磊,鲁东明. 特征金字塔多尺度全卷积目标检测算法[J]. 浙江大学学报(工学版), 2019, 53(3): 533-540.

Zhi-jie LIN,Zhuang LUO,Lei ZHAO,Dong-ming LU. Multi-scale convolution target detection algorithm with feature pyramid. Journal of ZheJiang University (Engineering Science), 2019, 53(3): 533-540.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.03.014 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I3/533

图 1 多尺度全卷积目标检测网络结构图

图 2 非极大值抑制算法流程图

图 3 包围盒位置精修模块

表 1 不同网络模型在标准数据集上的平均精度值比较

图 4 多尺度全卷积网络与Faster-RCNN在标准数据集上的召回率随交并比（IoU）变化折线图

图 5 多尺度全卷积网络模型与Faster-RCNN方法的微小目标检测效果对比

图 6 古代绘画图像数据集上的召回率随IoU的变化

图 7 多尺度全卷积网络模型与Fast-RCNN方法在古代绘画图像数据集上的目标检测效果对比

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