No-Service Rail Surface Defect Segmentation via Normalized Attention and
  Dual-scale Interaction
Gongyang Li Chengjun Han Zhi Liu
No-service rail surface defect (NRSD) segmentation is an essential way for
perceiving the quality of no-service rails. However, due to the complex and
diverse outlines and low-contrast textures of no-service rails, existing
natural image segmentation methods cannot achieve promising performance in NRSD
images, especially in some unique and challenging NRSD scenes. To this end, in
this paper, we propose a novel segmentation network for NRSDs based on
Normalized Attention and Dual-scale Interaction, named NaDiNet. Specifically,
NaDiNet follows the enhancement-interaction paradigm. The Normalized
Channel-wise Self-Attention Module (NAM) and the Dual-scale Interaction Block
(DIB) are two key components of NaDiNet. NAM is a specific extension of the
channel-wise self-attention mechanism (CAM) to enhance features extracted from
low-contrast NRSD images. The softmax layer in CAM will produce very small
correlation coefficients which are not conducive to low-contrast feature
enhancement. Instead, in NAM, we directly calculate the normalized correlation
coefficient between channels to enlarge the feature differentiation. DIB is
specifically designed for the feature interaction of the enhanced features. It
has two interaction branches with dual scales, one for fine-grained clues and
the other for coarse-grained clues. With both branches working together, DIB
can perceive defect regions of different granularities. With these modules
working together, our NaDiNet can generate accurate segmentation map. Extensive
experiments on the public NRSD-MN dataset with man-made and natural NRSDs
demonstrate that our proposed NaDiNet with various backbones (i.e., VGG,
ResNet, and DenseNet) consistently outperforms 10 state-of-the-art methods. The
code and results of our method are available at
https://github.com/monxxcn/NaDiNet.