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

作　　者：江玉朝 吉立新[1] 高超[1] 李邵梅[1] JIANG Yuchao;JI Lixin;GAO Chao;LI Shaomei(National Digital Switching System Engineering&Technological R&D Center,Zhengzhou 450002,China)

机构地区：[1]国家数字交换系统工程技术研究中心,河南郑州450002

出　　处：《网络与信息安全学报》2020年第2期116-124,共9页Chinese Journal of Network and Information Security

基　　金：国家自然科学基金资助项目(No.61601513)。

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。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：Logo检测 卷积神经网络 多尺度 区域生成网络 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]