An Explainable AI-Based Deep Learning Framework with Spatial and Multi-Head Attention for Bubble Detection in Tire Shearography Images: An Industrial Case Study

The safety and reliability of automobile tires depend directly on the early detection of internal defects, since undetected air bubbles between rubber layers can propagate into catastrophic in-service failures. Although digital shearography has become the preferred non-destructive testing technique for revealing such subsurface anomalies, its industrial interpretation still relies on manual expert assessment, which is slow, operator-dependent, and difficult to audit. This study proposes an explainable deep learning framework for automated bubble detection in tire shearography images. The framework couples a DenseNet201 backbone with two complementary attention modules: a Spatial Aggregation Pyramid (SAP) that refines multi-scale feature representations through dilated depth-wise separable convolutions, and a Multi-Head Attention (MHA) module that models long-range contextual dependencies, so that both fine-grained texture variations and global spatial cues are captured. The model was developed on a class-balanced set of 1162 shearography images (581 per class) from 174 tire treads at the Pirelli İzmit factory, obtained by randomly excluding part of the non-defective images. Under 5-fold cross-validation, the proposed DenseNet201 SAP MHA model reached a mean accuracy of 0.962 with a standard deviation of 0.006 and an AUC (area under the curve) of 0.9794, consistently outperforming the DenseNet201 and DenseNet201 SAP baselines across accuracy, precision, recall, and F1-score. The improvements were consistent across all five folds and were supported by statistical testing. To address the black-box concern inherent in safety-critical inspection, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) were integrated, demonstrating that the model bases its decisions on diagnostically relevant regions rather than background artefacts. The framework therefore delivers not only high bubble detection accuracy but also a transparent and auditable diagnostic tool suitable for industrial quality control.