Defect classification model for high-speed train wheelset treads based on SimAM and SpinalNet

doi:10.16265/j.cnki.issn1003-3033.2022.06.2563

Abstract

Abstract:

In order to address classification difficulty of tread defects for small sample tasks, a classification model based on SimAM and SpinalNet was proposed. Firstly, feature maps of each category of original images were extracted from pre-trained networks. Secondly, class features with stronger representation of defect images were extracted by using SimAM under limited training samples, local and overall semantics of feature maps were correlated by utilizing SpinalNet to obtain a strong distinguishing representation of defect class features. Finally, strong discriminating representation features were inputted to softmax classifier with L2 regularization, and classification results were obtained. The research shows that the accuracy rate of evaluation index of small sample tasks was 68.35% and 100%, respectively, which was better than current mainstream deep learning model.

Key words: wheelset tread, defect classification, simple parameter-free attention module(SimAM), spinal neural network (SpinalNet), l2 regularization

ZHANG Changfan, HU Xinliang, HE Jing, LIU Jianhua, HOU Na. Defect classification model for high-speed train wheelset treads based on SimAM and SpinalNet[J]. China Safety Science Journal, 2022, 32(6): 38-43.

Figures/Tables 7

Fig.1

Tab.1

ResNet50 structure

模块	模块1	模块2	模块3	模块4	模块5	输出
ResNet50	7×7,64, stride=2	$1 × 1, 64 3 × 3, 64 1 × 1, 256 × 3$	$1 × 1, 128 3 × 3, 128 1 × 1, 512 × 4$	$1 × 1, 256 3 × 3, 256 1 × 1, 1024 × 6$	$1 × 1, 512 3 × 3, 512 1 × 1, 2048 × 6$	GAP Fc Softmax
输出尺寸	112×112	56×56	28×28	14×14	7×7	1×1

Tab.1

Fig.2

Tab.2

Tab.3

Fig.3

Tab.4

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骨干网络	注意力机制	准确率1/%	准确率2/%	参数/ MByte
Resnet50	SE^[8]	60.76	98.73	25.70
	ECA^[9]	60.76	96.20	23.60
	CA^[10]	58.23	97.47	25.18
	EPSA^[11]	20.25	72.15	51.45
	PSA,模式P^[12]	59.49	98.73	31.99
	PSA,模式S^[12]	63.29	97.47	30.68
	FCA^[13]	55.70	94.94	24.65
	ULSAM^[14]	51.90	94.94	23.62
	NAM^[15]	58.23	92.41	23.61
	SimAM^[5]	64.56	100.00	23.60

骨干网络	预训练	SimAM	脊柱全连接	L2正则化	准确率1/%	准确率2/%	参数/MByte
ResNet50	—	—	—	—	41.77	94.94	23.60
	√	—	—	—	54.43	96.20	23.60
	√	√	—	—	64.56	100.00	23.60
	√	√	√	—	65.82	100.00	23.86
	√	√	√	√	68.35	100.00	23.86

模型	准确率 1/%	准确率2/%	参数/ MByte
VGG16^[17]	25.32	77.22	201.40
Mobilenetv1-&=1^[18]	18.99	89.87	3.24
Mobilenetv2^[19]	25.32	92.41	2.27
ResNet50^[20]	54.43	96.20	23.60
Inceptionv3^[21]	49.37	94.94	21.82
InceptionResNetV2^[22]	34.28	79.75	54.35
Xception^[23]	32.91	89.87	20.88
Vision Transformer^[24]	25.32	60.76	85.80
文中模型	68.35	100.00	23.86