Deep Gaussian Attention Network for Lumber Surface Defect Segmentation

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.