Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Sensors and Materials

DOI: 10.18494/sam5949

Authors: Nouf Abdullah Almujally, Ting Wu, Muhammad Waqas Ahmed, Ahmad Jalal, Hui Liu

Institutions: Air University, Korea University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $748

Funders: Deutsche Forschungsgemeinschaft, Princess Nourah Bint Abdulrahman University, Universität Bremen