Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (04): 142-148.doi: 10.13475/j.fzxb.20190604207

• Apparel Engineering • Previous Articles     Next Articles

Sewing gesture recognition based on improved YOLO deep convolutional neural network

WANG Xiaohua(), YAO Weiming, WANG Wenjie, ZHANG Lei, LI Pengfei   

  1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2019-06-18 Revised:2020-01-12 Online:2020-04-15 Published:2020-04-27

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Abstract:

A method of detecting and recognizing sewing gestures based on YOLO deep convolutional neural network was proposed to solve the problems of similar and less recognizable gestures in complex environments in the field of human-machine cooperation. Four complex sewing gestures were used to detect objects and construct a sewing gesture data set. By adding dense connection layer in the deep network of YOLOv3 low resolution, image feature transmission and reuse were enhanced, network performance was improved, and end-to-end sewing gesture was realized. The experimental results show that the trained model mean average precision is 94.45%, the intersection ratio is 0.87, and the harmonic mean is 0.885. By comparing region-convolutional neural networks, YOLOv2 and the original YOLOv3 algorithm, the detection accuracy of the improved method is significantly improved. At the same time, in the case of GPU acceleration, the average detection speed is 43.0 frames/s, and this fully satisfies the real-time detection of sewing gestures, and provides an algorithm basis for recognizing sewing gestures in complex environments.

Key words: sewing gesture recognition, target detection, YOLO deep convolutional neural network, garment sewing, human-machine cooperation

CLC Number: 

  • TP242.2

Fig.1

YOLO testing process"

Fig.2

YOLO improved network structure"

Tab.1

Number of images generated by data enhancement method"

手势类别 编号 原始图
像个数		 不同预处理方式下图像个数
颜色 亮度 旋转 模糊
内包缝 S1 120 120 1 100 160 100
卷边缝 S2 120 120 1 160 160 100
裁剪布料 S3 120 120 1 200 160 100
抽褶缝 S4 120 120 1 210 160 100

Tab.2

Network training parameters"

参数名 参数值
批样本大小(batch) 64个
批分割(subdivisions) 8个
迭代(iterations) 5 000次
学习率衰减步长(steps) 4 000次,4 500次
学习率衰减因子(scales) 0.1,0.1
动量(momentum) 0.9
权重衰减(decay) 0.000 5
非极大值阈值(nms) 0.25

Fig.3

Model loss curve"

Fig.4

Average cross-sectional ratio curve"

Fig.5

Average accuracy as a function of iterations number"

Fig.6

Confidence threshold"

Fig.7

Model P-R curve"

Tab.3

Experimental results of F1 at different data conditions"

数据量 500个 1 000个 2 000个 3 000个 4 000个
F1值 0.27 0.67 0.75 0.83 0.87

Tab.4

Experimental results under different lighting conditions"

时间 数据集A 数据集B
IOU值 F1值 IOU值 F1值
白天 0.858 0.806 0.874 0.862
傍晚 0.734 0.744 0.762 0.795
灯光 0.693 0.705 0.723 0.768

Fig.8

Detection effect of four sewing gestures under different lighting conditions"

Tab.5

Comparison of experimental results with different algorithms"

检测算法 mAP值/
%		 IOU值 F1值 检测速度/
(帧·s-1)
R-CNN 92.56 0.85 0.876 0.6
YOLOv2 90.04 0.76 0.865 46.0
YOLOv3 92.16 0.79 0.872 44.0
改进YOLOv3 94.45 0.87 0.885 43.0

Fig.9

Experimental results of different algorithms for four sewing gestures. (a)R-CNN;(b)YOLOv2;(c)YOLOv3;(d)Modified YOLOv3"

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