西装识别的深度学习方法

doi:10.13475/j.fzxb.20180504707

纺织学报 ›› 2019, Vol. 40 ›› Issue (04): 158-164.doi: 10.13475/j.fzxb.20180504707

西装识别的深度学习方法

刘正东¹(), 刘以涵², 王首人³

1.北京服装学院服装艺术与工程学院, 北京 100029
2.北京工业大学信息学部, 北京 100124
3.湖南大学信息科学与工程学院, 湖南长沙 410082

收稿日期:2018-05-21 修回日期:2019-01-08 出版日期:2019-04-15 发布日期:2019-04-16
作者简介:刘正东(1971—),男,副教授,博士。主要研究方向为服装工程数字化。E-mail:jsjlzd@bift.edu.cn
基金资助:
国家重点研发计划项目(2016YFB0302900);北京市科技计划课题项目(Z171100005017004)

Depth learning method for suit detection in images

LIU Zhengdong¹(), LIU Yihan², WANG Shouren³

1. Fashion Accessory Art and Engineering College, Beijing Institute of Fashion Technology, Beijing 100029, China
2. Information Department, Beijing University of Technology, Beijing 100124, China
3. College of Computer Scienceand Electronic Engineering, Hunan University, Changsha, Hunan 410082, China

Received:2018-05-21 Revised:2019-01-08 Online:2019-04-15 Published:2019-04-16

摘要/Abstract

摘要：

为准确而快速地对电商平台产品图像进行西装目标的分类检测,以3个主要的卷积网络深度学习框架即快速区域卷积神经网络、基于区域的全连接卷积网络和单次多盒检测为基础,首先通过实验分析其在服装图像分类识别中的效率和有效性,针对小目标识别困难和过拟合识别问题,提出基于尺寸分割和负样本的单次多盒检测(SSD)增强方法(DN-SSD);然后将图像分割为不同尺寸的子图突出服装目标,通过融合分类方法解决SSD算法对小目标识别不足的问题,并通过增强负样本以提高算法的场景适应能力。实验结果表明,该算法可有效地识别各种形态和大小的西装目标,识别准确率达到90%以上,并且能够方便地推广到服装其他品类的识别中。

关键词: 服装识别, 目标检测, 深度卷积神经网络, 深度学习, 单次多盒检测

Abstract:

In order to classify and detect the suit target in images of e-commerce platform accurately and quickly, an enhanced deep convolution network (DN-SSD) was proposed. First, three main frameworks faster region-convolutional networks (faster R-CNN), region-based fully convolution network(R-FCN) and single shot muti-box detection(SSD) were evaluated. An image was segmented into multiscale sub-images to highlight the suit target based on the SSD. Secondly, the problem of small target recognition was solved by the fusion of classification. The scene adaptability was enhanced by increasing number of negative samples. The experimental result shows that the algorithm can recognize various shapes and size of suit targets and achieves the accuracy over 90%. The method can also be generalized to other style of dress detection and location.

Key words: clothing recognition, target detection, deep convolution neural network, deep learning, single shot muti-box detection

中图分类号:

TP391.9

刘正东, 刘以涵, 王首人. 西装识别的深度学习方法[J]. 纺织学报, 2019, 40(04): 158-164.

LIU Zhengdong, LIU Yihan, WANG Shouren. Depth learning method for suit detection in images[J]. Journal of Textile Research, 2019, 40(04): 158-164.

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学习架构	特征抽取器	训练时间/h	召回率/%	精确率/%	识别时间/ms
Faster R-CNN	ResNet-101	102	68.80	88.12	565
R-FCN	ResNet-101	96	73.76	82.22	667
SSD	Inception V3	71	72.22	87.64	327

西装识别的深度学习方法

Depth learning method for suit detection in images

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