FALCON-HiMRAN: A Dual-stage RGB–D Sensor–based Scene Classification Framework with Cross-modal Fusion and Graph Reasoning

In this study, we address key limitations in RGB-depth (D) sensing systems, including depth noise, sensor misalignment, missing depth values, and performance degradation under low illumination.We propose a dual-stage RGB-D sensor-driven scene classification framework comprising feature-aligned lightweight cross-modal fusion (FALCON) and a hierarchical multiregion aggregation network (HiMRAN), the FALCON-HiMRAN, designed to enhance the reliability and interpretability of multimodal sensing systems.The proposed method integrates the data acquired from structured-light and time-of-flight RGB-D sensors and introduces the FALCON network to mitigate modality inconsistencies and sensor-induced noise.Furthermore, HiMRAN was developed to perform region-level reasoning by the graph-based modeling of spatial relationships.Experimental evaluation on benchmark RGB-D datasets demonstrates improved robustness under challenging sensing conditions such as occlusion, illumination variation, and depth degradation.The proposed framework contributes to the advancement of sensor-based perception systems by enabling more reliable scene understanding from imperfect multimodal sensor data.Remaining challenges include real-time deployment and the handling of extreme sensor noise in outdoor environments.