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
this https URL

在工业缺陷分割任务中，常用的度量指标是像素精度和交并比 (IoU)，然而模型的输出一致性 (或等效性) 经常被忽视。本文提出了一种全新的上 / 下采样层对抗 CNN 中传统采样层的不足，并通过自适应窗口模块和组件注意力模块来改善图像边界变化对等效性的影响，实验结果表明该方法在工业缺陷数据集上具有较高的等效性和分割性能。