  Surface defect inspection plays an important role in the process of
industrial manufacture and production. Though Convolutional Neural Network
(CNN) based defect inspection methods have made huge leaps, they still confront
a lot of challenges such as defect scale variation, complex background, low
contrast, and so on. To address these issues, we propose a joint
attention-guided feature fusion network (JAFFNet) for saliency detection of
surface defects based on the encoder-decoder network. JAFFNet mainly
incorporates a joint attention-guided feature fusion (JAFF) module into
decoding stages to adaptively fuse low-level and high-level features. The JAFF
module learns to emphasize defect features and suppress background noise during
feature fusion, which is beneficial for detecting low-contrast defects. In
addition, JAFFNet introduces a dense receptive field (DRF) module following the
encoder to capture features with rich context information, which helps detect
defects of different scales. The JAFF module mainly utilizes a learned joint
channel-spatial attention map provided by high-level semantic features to guide
feature fusion. The attention map makes the model pay more attention to defect
features. The DRF module utilizes a sequence of multi-receptive-field (MRF)
units with each taking as inputs all the preceding MRF feature maps and the
original input. The obtained DRF features capture rich context information with
a large range of receptive fields. Extensive experiments conducted on
SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves
promising performance in comparison with other state-of-the-art methods.
Meanwhile, our method reaches a real-time defect detection speed of 66 FPS.
