基于注意力数据增强的自动铺丝缺陷识别准确率提升方法

曹节强; 张立强; 李军利; 茅健; 刘钢

doi:10.12299/jsues.24-0074

基于注意力数据增强的自动铺丝缺陷识别准确率提升方法

doi: 10.12299/jsues.24-0074

上海工程技术大学机械与汽车工程学院, 上海 201620

详细信息

作者简介:
曹节强（1997 − ），男，硕士生，研究方向为复合材料缺陷检测。E-mail：m310121118@sues.edu.cn

通讯作者:
张立强（1979 − ），男，教授，博士，研究方向为航空智能制造技术、复合材料数控装备与工艺。E-mail：zhanglq@sues.edu.cn

中图分类号: V258 +.3
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出版历程
- 收稿日期: 2024-02-08
- 网络出版日期: 2025-09-30
- 刊出日期: 2025-06-30

An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

摘要

摘要: 针对自动铺丝过程中缺陷红外样本不足，获取困难，导致缺陷分类模型算法准确率低，难以有效识别的问题，提出一种注意力机制Wasserstein生成对抗网络数据增强方法。在生成器中引入CB-Attention注意力机制模块，提高捕获图像特征信息的能力，增大生成器的感受野，增强生成图像质量；采用批通道归一化，自适应地结合通道和批次维度的信息，以提高模型的训练速度和泛化能力。结果表明，所提注意力数据增强方法生成的样本具有多样性且高质量，加入小样本数据集后，有效提高了自动铺丝缺陷小样本数据集的识别准确率，验证了算法的有效性，为自动铺丝缺陷分类算法提供了数据基础。
- 自动铺丝缺陷识别 /
- 生成式对抗网络 /
- 数据增强 /
- 红外图像
Abstract: The scarcity of infrared defect samples and the difficulty in acquiring them during the automated tape laying process result in low classification accuracy and ineffective defect identification by classification model algorithm. To address this issue, a data augmentation method based on an attention mechanism Wasserstein generative adversarial network (WGAN) was proposed. A CB-Attention module was introduced into the generator to enhance its capability to capture image feature, expand the receptive field, and improve the quality of generated images without adding extra parameters. Batch channel normalization was employed to adaptively integrate information from both channel and batch dimensions, thereby imcreasing the model's training speed and generalization ability. Experimental results demonstrate that the samples generated by the attention-based data augmentation method are diverse and of high quality. Incorporating these samples into the small dataset of automated tape laying defects significantly improves defect recongnition accuracy, which validates the ffectiveness of the proposed algorithm and lays a data foundation for automated tape laying defect classification algorithms.
- automated fiber placement defect identification /
- generative adversarial network /
- data augmentation /
- infrared image

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图 1 GAN结构示意图

Figure 1. GAN structure diagram

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图 2 数据增强算法

Figure 2. Data enhancement algorithm

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图 3 生成器结构

Figure 3. Generator structure

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图 4 CB-Attention注意力机制模块

Figure 4. CB-Attention attention mechanism module

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图 5 判别器结构

Figure 5. Discriminator structure

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图 6 自动铺丝机

Figure 6. Automatic fiber placement machine

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图 7 生成的五类样本的特征分布

Figure 7. Feature distribution of five generated samples

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图 8 五类真实样本与生成样本分布对比

Figure 8. Distributions comparison of five types of real samples and generated samples

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图 9 不同算法的生成样本性能评估雷达图

Figure 9. Performance evaluation radar charts of generated samples by different algorithms

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图 10 3组实验ShuffleNet混淆矩阵

Figure 10. ShuffleNet confusion matrices of three experimental groups

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图 11 不同模型的查准率和查全率的差值

Figure 11. Difference between precision and recall of different models

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表 1 模型训练流程策略

Table 1. Model training process strategy

模型训练伪代码

In each training iteration:
　For k step do:
　　训练判别器其网络$ {{D}}_{\alpha } $(x)
　　从噪声分布$ {{P}}_{{z}}{(}{z}{)} $中抽取k个样本$ \left\{z^i\right\}_{i=1}^m $
　　从真实分布$ {\mathit{P}}_{{\mathrm{data}}}\left(x\right) $中抽取k个样本$ \left\{x^i\right\}_{i=1}^m $
　　计算真实图像 $\longrightarrow \operatorname{loss}_{{\boldsymbol{D}}(\text { real })} $
　　计算生成图像 $ \longrightarrow \operatorname{loss}_{{\boldsymbol{D}}(\text { fake })} $
　　样本向量$ \left\{\lambda^i\right\}_{i=1}^m $
　　$\lambda_i x^i+\left(1-\lambda_i\right) G\left(z^i\right) \longrightarrow \bar{x}_{i} $
　　通过降序更新生成器的权重$ {{ \omega }}_{{G}} $
　　$ \operatorname{Adam}\left(\nabla_{\omega_G}\left(\dfrac{1}{n}\displaystyle \sum_{i=1}^n D\left(G\left(z^i\right)\right)\right), \omega_G, \mu, \rho_1, \rho_2\right) \longrightarrow \omega_{G} $
　　通过降序更新判成器的权重$ {{ \omega }}_{{D}} $
　　$\operatorname{Adam}\left(\nabla _ { \omega _ { D } } \left(\dfrac{1}{n}\displaystyle \sum_{i=1}^n D\left(x_i\right)-D\left(G\left(z^i\right)\right)+k\left\|\nabla_{\bar{x}_i} D\left(\bar{x}_i\right)\right\|^p\right), \omega_D \rho_{1^{\prime}}, \rho_2\right) $
　　End for

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表 2 3组实验分类结果

Table 2. Classification results of three experimental groups

模型	第1组			第2组			第3组
模型	R	P	F₁	R	P	F₁	R	P	F₁
LeNet5	0.79	0.72	0.76	0.82	0.88	0.85	0.89	0.92	0.91
Wide ResNet	0.79	0.87	0.82	0.86	0.90	0.88	0.91	0.93	0.92
VGG16	0.85	0.75	0.78	0.88	0.92	0.90	0.95	0.96	0.95
NASNet	0.73	0.83	0.77	0.85	0.89	0.87	0.95	0.96	0.96
ShuffleNet	0.78	0.88	0.83	0.85	0.89	0.87	0.93	0.95	0.94

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