基于卷积神经网络的多尺度Logo检测算法

江玉朝; 吉立新; 高超; 李邵梅

doi:10.11959/j.issn.2096-109x.2020026

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基于卷积神经网络的多尺度Logo检测算法

学术论文 | 更新时间：2024-06-03

- 基于卷积神经网络的多尺度Logo检测算法
- Multi-scale Logo detection algorithm based on convolutional neural network
- 网络与信息安全学报 2020年6卷第2期页码：116-124
- 作者机构：
- 作者简介：
  
  [ "江玉朝（1994-），男，江苏盐城人，信息工程大学硕士生，主要研究方向为计算机视觉" ]
  [ "吉立新（1969-），男，江苏淮安人，博士，信息工程大学研究员、博士生导师，主要研究方向为通信与信息系统" ]
  [ "高超（1982-），男，河南郑州人，博士，信息工程大学助理研究员、博士生导师，主要研究方向为计算机视觉" ]
  [ "李邵梅（1982-），女，湖北钟祥人，博士，信息工程大学副研究员，主要研究方向为计算机视觉" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61601513)
- DOI：10.11959/j.issn.2096-109x.2020026
  中图分类号： TP391
- 网络首发：2020-04，
  
  纸质出版：2020-04-15
- 稿件说明：
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江玉朝, 吉立新, 高超, 等. 基于卷积神经网络的多尺度Logo检测算法[J]. 网络与信息安全学报, 2020,6(2):116-124.

Yuchao JIANG, Lixin JI, Chao GAO, et al. Multi-scale Logo detection algorithm based on convolutional neural network[J]. Chinese Journal of Network and Information Security, 2020, 6(2): 116-124.
江玉朝, 吉立新, 高超, 等. 基于卷积神经网络的多尺度Logo检测算法[J]. 网络与信息安全学报, 2020,6(2):116-124. DOI： 10.11959/j.issn.2096-109x.2020026.

Yuchao JIANG, Lixin JI, Chao GAO, et al. Multi-scale Logo detection algorithm based on convolutional neural network[J]. Chinese Journal of Network and Information Security, 2020, 6(2): 116-124. DOI： 10.11959/j.issn.2096-109x.2020026.

摘要

针对自然场景图像中多尺度Logo的检测需求，提出了一种基于卷积神经网络的多尺度Logo检测算法。该算法基于两阶段目标检测的实现思路，通过构建特征金字塔并采取逐层预测的方式实现多尺度候选区域的生成，通过融合卷积神经网络中的多层特征图以增强特征的表达能力。在FlickrLogos-32数据集上的实验结果显示，相比基线方法，所提算法能够提升生成候选区域的召回率，并且在保证大中尺度 Logo 检测精度的前提下，提升小尺度Logo的检测性能，验证了所提算法的优越性。

Abstract

Aiming at the requirements for multi-scale Logo detection in natural scene images

a multi-scale Logo detection algorithm based on convolutional neural network was proposed.The algorithm was based on the realization of two-stage object detection.By constructing feature pyramids and adopting layer-by-layer prediction

multi-scale region proposals were generated.The multi-layer feature maps in convolutional neural networks were fused to enhance the feature representation.The experimental results on the FlickrLogos-32 dataset show that compared with the baseline

the proposed algorithm can improve the recall rate of region proposals

and can improve the performance of small Logo detection while ensuring the accuracy of large and middle Logo

proving the superiority of the proposed algorithm.

关键词

Keywords

references

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