A Deep Multimodal Fusion Framework for Noncontact Temperature Detection in Ceramic Roller Kilns

Accurate temperature control in ceramic roller kilns is critical for ensuring product quality; however, it remains challenging due to nonlinear thermal dynamics and the spatial lag inherent in traditional contact-based sensors. To address the limitations of sparse wall-mounted thermocouples and optical interference in kiln images, this paper presents a multimodal spatiotemporal fusion network (MST-FusionNet) for noncontact temperature detection of ceramic bodies on roller tracks. The proposed network integrates in-furnace combustion image sequences with distributed thermocouple measurements. First, a physics-informed pseudo-heatmap generation strategy based on Gaussian distributions is introduced to align discrete thermocouple readings with visual features, enabling effective early-stage multimodal fusion. Second, a residual compensation mechanism uses thermocouple data as a stable reference to learn local temperature deviations from visual and temporal features. In addition, an attention-enhanced LSTM module is employed to model combustion dynamics and suppress unreliable frames caused by smoke and flame fluctuations. Experimental results on a real industrial dataset show that the proposed method achieves a mean absolute error of 0.9164 °C and a root mean squared error of 1.2422 °C, demonstrating better performance than single-modal methods and simple fusion baselines. The proposed framework exhibits stable spatial characteristics across different roller positions and helps bridge the spatial discrepancy between boundary measurements and the actual thermal state of ceramic products, providing an effective solution for temperature detection in roller kilns.