A Hierarchical Feature Fusion-based Method for Defect Recognition with a Small Sample

Yiping Gao, Liang Gao, Xinyu Li
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引用次数: 5

Abstract

As one of the breakthroughs in modern manufacturing, deep learning (DL) performs large-scale network architectures and achieves some outstanding performances in vision-based defect recognition. However, most of these large-scale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect large-scale samples. To overcome this problem, a hierarchical feature fusion-based method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low- and high- level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.
基于层次特征融合的小样本缺陷识别方法
深度学习(deep learning, DL)作为现代制造业的突破之一,在基于视觉的缺陷识别中实现了大规模的网络架构,并取得了一些突出的性能。然而,这些大规模的网络大多需要大样本进行训练,小样本可能会导致网络过拟合和崩溃。由于缺陷通常以低概率发生,因此收集大规模样品的成本很高。为了克服这一问题,提出了一种基于层次特征融合的小样本缺陷识别方法。该方法将预训练好的VGG16网络划分为不同的块,并从低、高层块中学习层次特征。结果优于其他方法。结果表明,该方法适合实际问题,可以较早地部署缺陷识别。
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