To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

中文翻译：

---

基于迁移学习的塑料垫片缺陷检测

为了自动检测塑料垫片缺陷，本研究设计并建立了一套基于GoogLeNet Inception-V2迁移学习的塑料垫片缺陷视觉检测装置。采用GoogLeNet Inception-V2深度卷积神经网络（DCNN）对塑料垫片的缺陷特征进行提取和分类，解决了塑料垫片表面缺陷众多、特征提取分类困难的问题。深度学习应用需要大量的训练数据来避免模型过拟合，但塑料垫片缺陷的数据集很少。为了解决这个问题，我们将数据增强应用于我们的数据集。最后，综合比较了三种卷积神经网络的性能。结果表明，GoogLeNet Inception-V2 迁移学习模型在更短的时间内具有更好的性能。这意味着它在本文使用的数据集上具有更高的准确性、可靠性和效率。