Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (11): 29-38.doi: 10.13475/j.fzxb.20210103610

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Real-time detection of inferior cocoons through model compression and receptive field enhancement

ZHANG Yinhui, YANG Hongkuan, LIU Qiang, HE Zifen   

  1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
  • Received:2021-01-15 Revised:2021-06-14 Online:2021-11-15 Published:2021-11-29

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Abstract:

The current inferior cocoons detection mainly depends on manual visual inspection, which leads to low work efficiency. Based on anchor parameter presetting, channel pruning and embedding receptive field block, an improved lightweight real time inferior cocoons detection model was proposed.Firstly, the model parameters of YOLOv3 were preset through K-means clustering analysis of the anchor suitable for inferior cocoons detection. Then, according to a preset pruning rate, the sparsely trained model was pruned based on batch normalization layer scaling factor to compress the size of the model. Finally, the receptive field block was embedded the pruned model to enlarge the receptive field of the model and enhance the discriminability and robustness of the model. The experimental results show that the proposed inferior cocoons real-time detection model is 46.90 M in size, and the mean average detection speed and precision reach 50.18 frames/s and 96.80% respectively. Compared with the original YOLOv3 model, the parameters are compressed by 79.96%, the mean average detection speed is increased by 60.63%, and the mean average detection precision is increased by 3.20%.

Key words: cocoons, inferior cocoons detection, YOLOv3 model, clustering analysis, model compression, receptive field block

CLC Number: 

  • TP391

Fig.1

YOLOv3 model structure diagram"

Fig.2

Visualization results of height/width ratio of anchor in inferior cocoons detection data"

Fig.3

Model channel pruning schematic diagram"

Fig.4

Calculation and derivation of RFB module receptive field"

Fig.5

YOLOv3-MC-RFB model structure diagram"

Fig.6

Inferior cocoons physical picture. (a)Thin shelled cocoon; (b)Crushed cocoon; (c)Little cocoon; (d)Yellow spotted cocoon"

Tab.1

Experimental parameters"

参数名称 数值
giou损失的系数(giou) 1.582
分类损失的系数(cls) 27.76
分类损失函数中正样本的权重(cls_pw) 1.446
有无物体损失的系数(obj) 21.35
有无物体损失函数中正样本的权重(obj_pw) 3.941
标签与锚点框的iou阈值(iou_t) 0.263 5
学习率(lr0) 0.002 324
余弦退火超参数(lrf) 0.000 1
学习率动量(momentum) 0.97
权重衰减系数(weight_decay) 0.000 456 9

Tab.2

Experimental results of different number of anchor"

锚点框数量 锚点框尺寸 模型大小/M 平均检测速度/(帧·s-1) mAP/%
3 (38,28),(31,35),(31,41) 234.00 31.14 96.10
6 (26,32),(33,26),(28,42),
(40,30),(33,38),(40,34)		 234.00 30.68 96.00
9 (27,29),(24,33),(33,26),
(26,40),(30,35),(42,28,
(38,33),(30,42),(35,39)		 234.00 30.46 95.90
12 (28,27),(34,24),(26,33),(27,39),
(35,31),(41,27),(31,37),(26,38),
(44,30),(33,41),(41,34),(37,38)		 235.00 30.38 95.20

Fig.7

Distribution of γ before (a) and after (b) sparse training"

Tab.3

Experimental results of different pruning rates"

剪枝率/% 模型大小/M 平均检测速度/
(帧·s-1)		 mAP/%
10.00 205.00 30.96 95.99
20.00 177.00 33.82 95.99
30.00 151.00 37.36 95.99
40.00 127.00 39.93 95.99
50.00 104.00 42.23 95.99
60.00 83.60 44.53 95.98
70.00 65.00 47.39 95.97
80.00 46.90 51.05 95.95

Tab.4

Model effects after embedding RFB"

RFB添加
的位置		 模型大
小/M		 平均检测速度/
(帧·s-1)		 mAP/%
4层后 46.90 47.52 96.70
11层后 46.90 49.87 96.60
36层后 46.90 50.18 96.80
61层后 46.90 50.04 96.20
74层后 46.90 49.57 96.10
86层后 46.90 48.47 96.70
98层后 46.90 48.77 96.10

Fig.8

Detection effect at different cocoons quantity distribution. (a) Original image; (b) Detection results"

Fig.9

Detection effect at different camera angles. (a) Original image; (b) Detection results"

Tab.5

Comparison of the experimental results with different detection model"

检测模型 模型大
小/M		 平均检测速度/
(帧·s-1)		 mAP/%
YOLOv3 234.00 31.24 93.80
YOLOv4 245.00 26.38 96.78
SSD 92.10 41.79 96.64
YOLOv3-MC-RFB 46.90 50.18 96.80

Fig.10

Inferior cocoons detection effect of Yolov3-MC-RFB model. (a) Single category of inferior cocoons; (b) Multiple categories of inferior cocoons"

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