Decision Fusion Network with Perception Fine-tuning for Defect
  Classification
Xiaoheng Jiang Shilong Tian Zhiwen Zhu Yang Lu Hao Liu Li Chen Shupan Li Mingliang Xu
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.