CG-fusion CAM: Online segmentation of laser-induced damage on
  large-aperture optics
Yueyue Han Yingyan Huang Hangcheng Dong Fengdong Chen Fa Zeng Zhitao Peng Qihua Zhu Guodong Liu
Online segmentation of laser-induced damage on large-aperture optics in
high-power laser facilities is challenged by complicated damage morphology,
uneven illumination and stray light interference. Fully supervised semantic
segmentation algorithms have achieved state-of-the-art performance, but rely on
plenty of pixel-level labels, which are time-consuming and labor-consuming to
produce. LayerCAM, an advanced weakly supervised semantic segmentation
algorithm, can generate pixel-accurate results using only image-level labels,
but its scattered and partially under-activated class activation regions
degrade segmentation performance. In this paper, we propose a weakly supervised
semantic segmentation method with Continuous Gradient CAM and its nonlinear
multi-scale fusion (CG-fusion CAM). The method redesigns the way of
back-propagating gradients and non-linearly activates the multi-scale fused
heatmaps to generate more fine-grained class activation maps with appropriate
activation degree for different sizes of damage sites. Experiments on our
dataset show that the proposed method can achieve segmentation performance
comparable to that of fully supervised algorithms.