基于模型压缩的安瓿瓶外观检测仿真研究

doi:10.16182/j.issn1004731x.joss.22-FZ0925

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (12): 2575-2583.doi: 10.16182/j.issn1004731x.joss.22-FZ0925

基于模型压缩的安瓿瓶外观检测仿真研究

朱志豪(), 王艳(), 纪志成

江南大学教育部物联网技术应用工程中心，江苏无锡 214122

收稿日期:2022-08-07 修回日期:2022-09-09 出版日期:2022-12-31 发布日期:2022-12-21
通讯作者: 王艳 E-mail:Zhuzhihaozzhyx@163.com;wangyan@jiangnan.edu.cn
作者简介:朱志豪(1997-)，男，硕士生，研究方向为深度学习目标检测。E-mail：Zhuzhihaozzhyx@163.com
基金资助:
国家重点研发计划(2018YFB1701903);国家自然科学基金(61973138)

Simulation Research on Appearance Detection of Ampoules Based on Lightweight Network and Model Compression

Zhihao Zhu(), Yan Wang(), Zhicheng Ji

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2022-08-07 Revised:2022-09-09 Online:2022-12-31 Published:2022-12-21
Contact: Yan Wang E-mail:Zhuzhihaozzhyx@163.com;wangyan@jiangnan.edu.cn

摘要/Abstract

摘要：

针对目标检测网络模型规模大、参数量冗余，导致安瓿瓶外观缺陷检测模型难以部署到边缘设备的问题，提出一种基于轻量化网络和模型压缩的Faster R-CNN安瓿瓶外观缺陷检测算法。以MobileNet-V2作为主干网络，利用模型剪枝策略裁剪卷积网络中冗余的通道；通过饱和截取映射将浮点型参数量化为整型；利用知识蒸馏恢复压缩后网络的检测精度。在自主构建的安瓿瓶外观缺陷数据集进行测试，模型体积减少了69.6%，平均精度为89.3%。仿真结果表明：压缩后的目标检测模型满足实际应用中安瓿瓶外观检测要求。

关键词: 目标检测, 模型剪枝, 参数量化, 知识蒸馏, Faster R-CNN

Abstract:

Aiming at the large scale and redundant parameters of target detection network model, which result in the difficult to deploy the ampoule bottle appearance defect detection model to edge devices, an LC-Faster R-CNN defect detection algorithm based on lightweight network and model compression is proposed. MobileNet-V2 is used as the backbone, and the redundant channels in the convolutional network are trimmed by model pruning strategy. The floating-point parameters are quantized into integers through saturation truncation mapping. Knowledge distillation is used to restore the accuracy of the compressed network. Tested on the self-built ampoule appearance defect dataset, the model volume is reduced by 69.6% and the average accuracy is 89.3%. The simulation results show that the compressed target detection model can meet the requirements of the appearance detection of ampoules in practical applications.

Key words: object detection, model pruning, parameter quantization, knowledge distillation, Faster R-CNN

中图分类号:

TP391.4

朱志豪, 王艳, 纪志成. 基于模型压缩的安瓿瓶外观检测仿真研究[J]. 系统仿真学报, 2022, 34(12): 2575-2583.

Zhihao Zhu, Yan Wang, Zhicheng Ji. Simulation Research on Appearance Detection of Ampoules Based on Lightweight Network and Model Compression[J]. Journal of System Simulation, 2022, 34(12): 2575-2583.

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参数名称		参数值
Batch_Size		8
Learning rate		0.005
Step_Size		3
Momentum		0.9

剪枝次数	mAP/%	MADD/M	FLOPs/G	params/M
量化	86.38	394.4	1.28	0.70
0	91.00	1458.3	4.64	2.30
1	90.02	985.2	3.15	1.45
2	88.72	708.3	2.27	1.00
4	86.44	433.4	1.40	0.70

Input	Operator	压缩前参数量	压缩后参数量	Output
32×128×128	Conv	0.001	0.001	32×128×128
32×128×128	BN	0	0	32×128×128
32×128×128	Bottleneck	0.003	0.002	16×128×128
16×128×128	Bottleneck Bottleneck	0.006 0.009	0.003 0.004	24×128×128
24×128×128	Bottleneck Bottleneck Bottleneck	0.010 0.015 0.015	0.005 0.007 0.003	32×64×64
32×64×64	Bottleneck	0.021	0.004	64×32×32
	Bottleneck	0.054	0.010
	Bottleneck	0.054	0.010
	Bottleneck	0.054	0.010
64×32×32	Bottleneck	0.073	0.018	96×32×32
	Bottleneck	0.118	0.020
	Bottleneck	0.118	0.007
96×32×32	Bottleneck	0.155	0.010	160×16×16
	Bottleneck	0.320	0.019
	Bottleneck	0.320	0.019
160×16×16	Block	0.526	0.080	320×16×16
320×16×16	Conv	0.410	0.410	1 280×16×16
1 280×16×16	BN	0.003	0.003	1 280×16×16
1 280×16×16	Linear	0.013	0.013	16×10

蒸馏前后	mAP
蒸馏前	86.38
蒸馏后	89.30

检测类别	精确度	漏检率	误检率
平均	89.3	12.7	10.7
安瓿瓶	100	0	0
碳化黑点	91.5	8.4	8.5
头部缺陷	81.1	24.8	18.9
裂纹	84.6	17.6	15.4

基于模型压缩的安瓿瓶外观检测仿真研究

Simulation Research on Appearance Detection of Ampoules Based on Lightweight Network and Model Compression

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