Deep learning has been widely used in recent years for surface defect detection because of its excellent performance. However, current deep-learning-based approaches still remain a challenging problem in sawn lumber defect inspection because different lumber defects often keep similar textures and colors to different growing environments surface stains, etc. Meanwhile, the same defect often shows different characteristics. Furthermore, lumber defects have ambiguous boundaries or regions, and large-scale variations in size and shape. To address these problems, we have developed a deep Gaussian attention network via Deeplabv3+ for lumber surface defect segmentation. This network introduces an attention network via a transformer to capture the long-distance dependence for global information extraction, which can efficiently improve mis-segmentation since different lumber defects are very similar in some local regions. Furthermore, we introduce a Gaussian module into the channel attention module and positional attention module, respectively, to reassign and reactivate the minor semantic features for hard example mining so that all regions of defects can be activated except the key regions for efficient semantic segmentation. Finally, the activated global information and local information in astrous spatial pyramid pooling (ASPP) are integrated to achieve efficient feature extraction. Experimental results demonstrate the proposed network can efficiently address the ambiguous defect regions and irregular sizes and shapes of sawn lumber surface defect segmentation.

中文翻译：

---

用于木材表面缺陷分割的深度高斯注意力网络

深度学习因其优异的性能近年来被广泛应用于表面缺陷检测。然而，当前基于深度学习的方法在锯材缺陷检测中仍然是一个具有挑战性的问题，因为不同的木材缺陷往往会因不同的生长环境、表面污渍等而保持相似的纹理和颜色。同时，相同的缺陷往往表现出不同的特征。此外，木材缺陷具有不明确的边界或区域，以及尺寸和形状的大范围变化。为了解决这些问题，我们通过 Deeplabv3+ 开发了一个深度高斯注意力网络，用于木材表面缺陷分割。该网络通过变压器引入注意力网络来捕获全局信息提取的长距离依赖性，这可以有效地改善错误分割，因为不同的木材缺陷在某些局部区域非常相似。此外，我们分别在通道注意模块和位置注意模块中引入高斯模块，以重新分配和重新激活硬样本挖掘的次要语义特征，以便激活除关键区域之外的所有缺陷区域，以进行有效的语义分割。最后，整合星体空间金字塔池化（ASPP）中激活的全局信息和局部信息，实现高效的特征提取。实验结果表明，所提出的网络可以有效地解决锯材表面缺陷分割的模糊缺陷区域以及不规则尺寸和形状。