Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% $F_{\beta}^{w}$, 93.55% $S_\alpha$, and 97.35% $E_\phi$) on SD-saliency-900 while running 272fps on a single gpu.

表面缺陷检测是一个具有挑战性的任务，传统高精度的缺陷检测方法通常需要昂贵的计算和存储成本。因此，本文提出了一种基于编码-解码结构的全局上下文聚合网络(GCANet)来实现轻量级表面缺陷的显著性检测。GCANet在轻量级骨干网络的顶部引入了一个新颖的Transformer编码器，并通过一种新颖的深度自注意(DSA)模块捕获全局上下文信息。此外，本文还引入了一种新颖的通道参考注意(CRA)模块，用于在解码器块之前加强来自不同层级的多级特征的表示。实验结果表明，与其他17种最先进方法相比，GCANet在准确性和运行效率之间取得了更好的平衡，在三个公开的缺陷数据集上取得了竞争性的结果。

全局上下文聚合网络用于轻量级表面缺陷显著性检测