MINet: Multiscale Interactive Network for Real-Time Salient Object Detection of Strip Steel Surface Defects

The automated surface defect detection is a fundamental task in industrial production, and the existing saliency-based works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, which heavily limits the practical applications. To this end, we devise a multiscale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales, respectively. Particularly, the MI module can provide satisfactory characterization for defect regions with fewer parameters.…

• NN
  National Natural Science Foundation of China

  Awards: 62171269, 62271180