Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
Qu Z(屈震)1; Tao X(陶显)1; Shen F(沈飞)1; Zhang ZT(张正涛)1; Li T(李涛)2
Source PublicationIEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
2023-10
Pages1-17
Abstract
In industrial defect segmentation tasks, while pixel
accuracy and Intersection over Union (IoU) are commonly
employed metrics to assess segmentation performance, the output
consistency (also referred to as equivalence) of the model is
often overlooked. Even a small shift in the input image can
yield significant fluctuations in the segmentation results. Existing
methodologies primarily focus on data augmentation or antialias
ing to enhance the network’s robustness against translational
transformations, but their shift equivalence performs poorly on
the test set or is susceptible to nonlinear activation functions.
In addition, the variations in boundaries resulting from the trans
lation of input images are consistently disregarded, thus imposing
further limitations on the shift equivalence. In response to this
particular challenge, a novel pair of downsampling/upsampling
layers called component attention polyphase sampling (CAPS)
is proposed as a replacement for the conventional sampling
layers in CNNs. To mitigate the effect of image boundary
variations on the equivalence, an adaptive windowing module
is designed in CAPS to adaptively filter out the border pixels
of the image. Furthermore, a component attention module is
proposed to fuse all downsampled features to improve the
segmentation performance. The experimental results on the micro
surface defect (MSD) dataset and four real-world industrial
defect datasets demonstrate that the proposed method exhibits
higher equivalence and segmentation performance compared to
other state-of-the-art methods. Our code will be available at
https://github.com/xiaozhen228/CAPS.
Indexed BySCI
Language英语
Sub direction classification人工智能+制造
planning direction of the national heavy laboratory先进智能应用与转化
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57185
Collection中科院工业视觉智能装备工程实验室_精密感知与控制
Corresponding AuthorQu Z(屈震); Tao X(陶显)
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Institute of Automation, Gansu Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qu Z,Tao X,Shen F,et al. Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2023:1-17.
APA Qu Z,Tao X,Shen F,Zhang ZT,&Li T.(2023).Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,1-17.
MLA Qu Z,et al."Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2023):1-17.
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