  The automated surface defect detection is a fundamental task in industrial
production, and the existing saliencybased 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 multi-scale 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. Embarking on this module, we propose a
lightweight Multi-scale Interactive Network (MINet) to conduct real-time
salient object detection of strip steel surface defects. Comprehensive
experimental results on SD-Saliency-900 dataset, which contains three kinds of
strip steel surface defect detection images (i.e., inclusion, patches, and
scratches), demonstrate that the proposed MINet presents comparable detection
accuracy with the state-of-the-art methods while running at a GPU speed of
721FPS and a CPU speed of 6.3FPS for 368*368 images with only 0.28M parameters.
The code is available at https://github.com/Kunye-Shen/MINet.
