  Surface defect inspection is an important task in industrial inspection. Deep
learning-based methods have demonstrated promising performance in this domain.
Nevertheless, these methods still suffer from misjudgment when encountering
challenges such as low-contrast defects and complex backgrounds. To overcome
these issues, we present a decision fusion network (DFNet) that incorporates
the semantic decision with the feature decision to strengthen the decision
ability of the network. In particular, we introduce a decision fusion module
(DFM) that extracts a semantic vector from the semantic decision branch and a
feature vector for the feature decision branch and fuses them to make the final
classification decision. In addition, we propose a perception fine-tuning
module (PFM) that fine-tunes the foreground and background during the
segmentation stage. PFM generates the semantic and feature outputs that are
sent to the classification decision stage. Furthermore, we present an
inner-outer separation weight matrix to address the impact of label edge
uncertainty during segmentation supervision. Our experimental results on the
publicly available datasets including KolektorSDD2 (96.1% AP) and
Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the
proposed method.
