MFNet: A Novel Multi-level Feature Fusion Network with Multi-branch Structure for Surface Defect Detection

Surface defect detection is an essential topic in the industrial inspection field. Many methods based on machine vision (MV) have been applied. However, it is still a challenging task due to the complexity of defects, including low-contrast, small objects, and irregular geometric boundaries. To deal with these problems, this paper proposes a novel multi-level feature fusion network (MFNet) with  the multi-branch structure for surface defect detection. Firstly, we extract low- and high-level features via the encoder based on ResNet34. Secondly, an improved atrous spatial pyramid pooling module is adapted to expand the receptive field of low-level features. Then, the decoder adopts a multi-branch structure to fuse multi-level features for details, and a global attention module is introduced to strengthen the effectiveness of feature fusion and detection accuracy. Finally, the optimal result from multiple outputs can be obtained by multi-branch. Extensive experiments are carried out on three representative defect datasets: CrackForest, Kolektor, and RSDDs. The quantitatively contrastive experiments prove that our method enjoys a better semantic segmentation performance in industrial defect detection, outperforming four excellent semantic segmentation networks (CrackForest: Accuracy-98.54%, F1-Score-74.71%, IoU-60.22%; Kolektor: Accuracy-99.82%, F1-Score-83.09%, IoU-71.38%; Type-Ⅰ RSDDs: Accuracy-99.79\%, F1-Score-85.09%, IoU-74.84%).