Protective Material Crease Detection with Small Sample-driven Feature Segmented Neural Network

doi:10.12382/bgxb.2022.0884

Abstract

Abstract:

Stab-proof clothing can effectively protect life safety in terrorist attacks, medical problems, breaking the law and committing crimes and other incidents, but the mechanical creases of tab-proof clothing are easily produced in production and wearing. Based on the demand for rapid detection of crease defects of protective materials, a small sample-driven feature segmented neural network structure is proposed innovatively in the image recognition method, and the rapid and accurate detection of crease defects is realized. By introducing the attention mechanism and the depth-separable convolution module and giving the loss function and the optimizer two typical parameters, the detection accuracy and efficiency of the feature segmented neural network are improved comprehensively. A geometric information annotation algorithm is proposed and a visual detection platform is built for defect detection of protective materials, realizing the automatic and accurate location of mechanical creases and the output of geometric information. The results show that the accuracy of the model can reach 96.19%, and the annotation error of geometric information is less than 2%. The excellent visual detection function can be extended to the field of large-scale engineering automatic detection. The research work lays a foundation for constructing a protective performance prediction model of the stab-proof equipment with crease defects.

Key words: protective material, mechanical creases detection, feature segmented neural network, geometric information annotation

CLC Number:

LIU Mengzhen, HUANG Guangyan, ZHANG Hong, ZHOU Hongyuan, LIU Siyu. Protective Material Crease Detection with Small Sample-driven Feature Segmented Neural Network[J]. Acta Armamentarii, 2024, 45(3): 963-974.

Figures/Tables 21

References 29

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硬件	主要参数	厂商
工业相机	分辨率4022×3036	杭州海康威视数字技术股份有限公司
镜头	焦距8mm	杭州海康威视数字技术股份有限公司
工业光源	300mm×300mm	杭州海康威视数字技术股份有限公司
光源控制器	2通道,24V光源	大康控股集团有限公司
相机夹具	最大距离100mm	定制
可拼接镀铬精密管	ϕ25×400mm	定制
光源架	280mm×180mm	定制
透明板	280mm×180mm	定制
底板	300mm×300mm	定制

硬件	主要参数	厂商
工业相机	分辨率4022×3036	杭州海康威视数字技术股份有限公司
镜头	焦距8mm	杭州海康威视数字技术股份有限公司
工业光源	300mm×300mm	杭州海康威视数字技术股份有限公司
光源控制器	2通道,24V光源	大康控股集团有限公司
相机夹具	最大距离100mm	定制
可拼接镀铬精密管	ϕ25×400mm	定制
光源架	280mm×180mm	定制
透明板	280mm×180mm	定制
底板	300mm×300mm	定制

层	网络	输入通道数	输出通道数	核尺寸	图像尺寸
	二维卷积层	3	32	5×5	1024×1024
1	二维卷积层	32	32	5×5	1024×1024
	池化层	32	32	2×2	512×512
	二维卷积层	32	64	5×5	512×512
2	二维卷积层	64	64	5×5	512×512
	二维卷积层	64	64	5×5	512×512
	池化层	64	64	2×2	256×256
	二维卷积层	64	64	5×5	256×256
	二维卷积层	64	64	5×5	256×256
3	二维卷积层	64	64	5×5	256×256
	二维卷积层	64	64	5×5	256×256
	池化层	64	64	2×2	128×128
注意力层	二维卷积层	64	4	1×1	128×128
	二维卷积层	4	64	1×1	128×128
4	二维卷积层	64	1024	15×15	128×128
5	二维卷积层	1024	1	1×1	128×128

层	网络	输入通道数	输出通道数	核尺寸	图像尺寸
	二维卷积层	3	32	5×5	1024×1024
1	二维卷积层	32	32	5×5	1024×1024
	池化层	32	32	2×2	512×512
	二维卷积层	32	64	5×5	512×512
2	二维卷积层	64	64	5×5	512×512
	二维卷积层	64	64	5×5	512×512
	池化层	64	64	2×2	256×256
	二维卷积层	64	64	5×5	256×256
	二维卷积层	64	64	5×5	256×256
3	二维卷积层	64	64	5×5	256×256
	二维卷积层	64	64	5×5	256×256
	池化层	64	64	2×2	128×128
注意力层	二维卷积层	64	4	1×1	128×128
	二维卷积层	4	64	1×1	128×128
4	二维卷积层	64	1024	15×15	128×128
5	二维卷积层	1024	1	1×1	128×128

层	网络	输入通道数	输出通道数	核尺寸	图像尺寸
深度可分离卷积层	二维卷积层	1 025	1 025	5×5	128×128
	二维卷积层	1 025	8	5×5	128×128
	池化层	8	8	2×2	64×64
	二维卷积层	8	8	5×5	64×64
	二维卷积层	8	16	5×5	64×64
	池化层	16	16	2×2	32×32
	二维卷积层	16	32	5×5	32×32
1	线性层	66	1	—	1×1