@Article{cmc.2026.089764,
AUTHOR = {Ye Li, Haotian Ning, Tong Xiao, Xiaoke Su, Weiwei Zhang},
TITLE = {Coarse-to-Fine Alignment and Reliability-Driven Fusion for Multispectral Object Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28374},
ISSN = {1546-2226},
ABSTRACT = {To address the challenges of cross-modal spatial misalignment, modality reliability discrepancies, and inadequate supervision in visible-infrared (RGB-IR) multispectral object detection, this paper proposes Coarse-to-Fine Alignment and Reliability-Driven Fusion Network (CFR-Net), a robust framework designed for complex real-world environments. First, we introduce the Scale-aware Offset Alignment and Residual (SOAR) module. By employing a coarse-to-fine offset estimation mechanism, SOAR effectively rectifies global displacements and compensates for local residual misalignments, while leveraging high-frequency residual information to enhance fine-grained representations in small-object regions. Based on the aligned features, the Reliability-aware Quality-guided Fusion (RQF) module is further designed, which adaptively adjusts the fusion weights of RGB and IR features according to region-level modality quality and cross-modal conflict intensity, thereby enhancing reliable complementary information and suppressing low-quality or conflicting responses. Furthermore, an Alignment-aware Fusion Consistency (AFC) Loss is formulated to provide explicit process-level supervision by jointly optimizing feature alignment, fusion consistency, and hard-sample detection, thereby ensuring a more principled learning process that extends beyond standard detection objectives. Experimental results on the DroneVehicle and M3FD datasets demonstrate that a mean Average Precision at an intersection over union (IoU) threshold of 0.50 (mAP50) of 82.1% and 85.7% is achieved by CFR-Net, with a compact architecture of only 6.5M parameters and 16.9 giga floating-point operations (GFLOPs). Consequently, CFR-Net provides a robust and computationally efficient solution for multispectral object detection, achieving competitive performance with minimal architectural overhead.},
DOI = {10.32604/cmc.2026.089764}
}