Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
文献类型：期刊论文
| 作者 | Qu Z(屈震)2 ; Tao X(陶显)2; Shen F(沈飞)2; Zhang ZT(张正涛)2; Li T(李涛)1
|
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2023-10 |
| 页码 | 1-17 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57185]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Qu Z(屈震); Tao X(陶显) |
| 作者单位 | 1.Institute of Automation, Gansu Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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