A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement

Consistent detection of cracks in engineering structures is essential for maintaining structural integrity. Deep neural networks perform well in this discipline, although their pixel-level labeling reliance increases labeling costs. Thus, weakly supervised learning methods have emerged. However, their labels are substantially worse quality than those of manual labeling. Current deep neural network visual interpretation approaches have issues including erroneous target localization. This study proposes an Affine Transformation and Pseudo Label Refinement (AT-CAM) method. The methodology comprises three phases: the initial phase employs a geometric enhancement strategy to produce a sequence of enhanced images from the input images, utilizing the Axiom-based Grad-CAM (XGradCAM) algorithm to generate class activation maps for each image, which are subsequently amalgamated into a unified saliency map; in the subsequent phase, the information flow pathways in the subsampling of the convolutional layer are modified by a designated Hook. The information flow in the samples is utilized to invert and eliminate the checkerboard noise produced during integration spatially; in the third stage, a dynamic range compression mechanism is employed to augment the prominence of the cracked areas by compressing the highlighted regions in the saliency map and diminishing the influence of background noise. The experimental results indicate that the method proposed in this study increases segmentation accuracy by 7.2% relative to the original baseline, markedly improves the visual interpretability of deep neural networks, and offers a novel, efficient, cost-effective, and interpretable approach for detecting structural cracks in engineering.