Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (04): 158-164.doi: 10.13475/j.fzxb.20180504707

• Management & Information • Previous Articles     Next Articles

Depth learning method for suit detection in images

LIU Zhengdong1(), LIU Yihan2, WANG Shouren3   

  1. 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

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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

CLC Number: 

  • TP391.9

Tab.1

Comparison of suit recognition by three methods"

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

Fig.1

SSD framework"

Fig.2

Pyramid feature extraction"

Fig.3

Inter section over union"

Fig.4

Failure cases. (a)Target failure; (b)Target loss; (c)Target overlap"

Fig.5

Usual dress forms to suit. (a)With a tie;(b)With a bow tie; (c)No tie; (d)Not fasten"

Fig.6

Labeling target box. (a)Box of tie; (b)Box of bow tie"

Fig.7

Recognition process of DN-SSD"

Fig.9

Detection results. (a)Single target detection 1; (b)Single target detection 2; (c)Multi target detection; (d)Small target detection"

Fig.8

Curve of loss function value in training process"

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