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

TL;DR

This paper introduces CG-fusion CAM, a weakly supervised segmentation method that effectively segments laser-induced damage on large optics using only image-level labels, achieving results comparable to fully supervised methods.

Contribution

It proposes a novel CG-fusion CAM technique that improves weakly supervised segmentation by enhancing class activation maps through gradient redesign and multi-scale fusion.

Findings

Achieves segmentation accuracy comparable to fully supervised methods.

Effectively handles complex damage morphology and uneven illumination.

Reduces the need for pixel-level labeling in damage segmentation.

Abstract

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.