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Online segmentation of laser-induced damage on large-aperture optics inhigh-power laser facilities is challenged by complicated damage morphology,uneven illumination and stray light interference. Fully supervised semanticsegmentation algorithms have achieved state-of-the-art performance, but rely onplenty of pixel-level labels, which are time-consuming and labor-consuming toproduce. LayerCAM, an advanced weakly supervised semantic segmentationalgorithm, can generate pixel-accurate results using only image-level labels,but its scattered and partially under-activated class activation regionsdegrade segmentation performance. In this paper, we propose a weakly supervisedsemantic segmentation method with Continuous Gradient CAM and its nonlinearmulti-scale fusion (CG-fusion CAM). The method redesigns the way ofback-propagating gradients and non-linearly activates the multi-scale fusedheatmaps to generate more fine-grained class activation maps with appropriateactivation degree for different sizes of damage sites. Experiments on ourdataset show that the proposed method can achieve segmentation performancecomparable to that of fully supervised algorithms.

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics