Investigating Shift Equivalence of Convolutional Neural Networks in
  Industrial Defect Segmentation
Zhen Qu Xian Tao Fei Shen Zhengtao Zhang Tao Li
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 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 anti-aliasing 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. Additionally, the variations in boundaries resulting from
the translation of input images are consistently disregarded, thus imposing
further limitations on the shift equivalence. In response to this particular
challenge, a novel pair of down/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.