  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.
