Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture that integrates three key innovative modules: (1) the Spectral Hierarchical Perception Architecture (SHPA), which adopts a dual-stream separated structure with independently parameterized feature extraction paths for visible and infrared modalities; (2) the Spectral Recursive Fusion Module (SRFM), which combines hybrid attention mechanisms with recursive progressive fusion strategies to achieve deep feature interaction through parameter-sharing multi-round recursive processing; and (3) the Spectral Target Perception Enhancement Module (STPEM), which adaptively enhances target region representation and suppresses background interference. Extensive experiments on the VEDAI, FLIR-aligned, and LLVIP datasets demonstrate that SDRFPT-Net significantly outperforms state-of-the-art methods, with improvements of 2.5% in mAP50 and 5.4% in mAP50:95 on VEDAI, 11.5% in mAP50 on FLIR-aligned, and 9.5% in mAP50:95 on LLVIP. Ablation studies further validate the effectiveness of each proposed module. The proposed method provides an efficient and robust solution for multispectral object detection in remote sensing image interpretation, making it particularly suitable for all-weather monitoring applications from aerial and satellite platforms, as well as in intelligent surveillance and autonomous driving domains.

Limited by the imaging capabilities of sensors, research based on single modality is difficult to cope with faults and dynamic perturbations in detection. Effective multispectral object detection, which can achieve better detection accuracy by fusing visual information from different modalities, has attracted widespread attention. However, most of the existing methods adopt simple fusion mechanisms, which fail to utilize the complementary information between modalities while lacking the guidance of a priori knowledge. To address the above issues, we propose a novel background-aware cross-attention multiscale fusion network (BA-CAMF Net) to achieve adaptive fusion in visible and infrared images. First, a background-aware module is designed to calculate the light and contrast to guide the fusion. Then, a cross-attention multiscale fusion module is put forward to enhance inter-modality complement features and intra-modality intrinsic features. Finally, multiscale feature maps from different modalities are fused according to background-aware weights. Experimental results on LLVIP, FLIR, and VEDAI indicate that the proposed BA-CAMF Net achieves higher detection accuracy than the current State-of-the-Art multispectral detectors.

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets.

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications.

Multispectral object detection combining visible and infrared imaging has emerged as a crucial technology for all-day and all-weather surveillance systems. However, effectively integrating complementary information from different spectral domains remains challenging. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture for multispectral object detection that addresses these challenges through three innovative modules. First, we introduce a Spectral Hierarchical Perception Architecture (SHPA) based on YOLOv10, which employs a dual-stream structure to extract domain-specific features from visible and infrared modalities. Second, a Spectral Recursive Fusion Module (SRFM) facilitates deep cross-modal feature interaction through a hybrid attention mechanism that integrates self-attention, cross-modal attention, and channel attention, coupled with a parameter-efficient recursive progressive fusion strategy. Third, a Spectral Target Perception Enhancement Module (STPEM) improves target region representation and suppresses background interference using lightweight mask prediction. Extensive experiments on the FLIR-aligned and LLVIP datasets demonstrate SDRFPT-Net's superior performance, achieving state-of-the-art results with 0.785 mAP50 and 0.426 mAP50:95 on FLIR-aligned, and 0.963 mAP50 and 0.706 mAP50:95 on LLVIP. Comprehensive ablation studies validate the effectiveness of each proposed component. The findings suggest that SDRFPT-Net offers a promising solution for reliable multispectral object detection in challenging environments, making it valuable for applications in autonomous driving, security surveillance, and remote sensing.

RGBT tracking holds irreplaceable value in unmanned aerial vehicle (UAV) ground observation missions, effectively supporting scenarios such as nighttime monitoring and low-altitude reconnaissance. However, existing frameworks based on CNNs or Transformers face inherent trade-offs between interaction capabilities and computational efficiency. Furthermore, current methods perform poorly in challenging scenarios involving target scale variations and rapid motion from UAV perspectives. To address these issues, this paper proposes a novel multimodal interaction and fusion Mamba network (MIFMNet), which achieves fundamental innovations relative to existing RGB-T fusion trackers and recent Mamba-based tracking methods. Different from existing RGB-T trackers that rely on CNN’s local convolution or Transformer’s quadratic-complexity self-attention for cross-modal fusion, MIFMNet departs from these architectures and designs modality-adaptive interaction mechanisms based on Mamba, fully leveraging the complementary information while resolving the efficiency-accuracy trade-off. Specifically, this paper designs the scale differential enhanced Mamba (SDEM), which expands the receptive field through multiscale parallel convolutions while amplifying complementary information via differential strategies to enhance feature responses to scale-varying objects. Furthermore, we propose flow-guided multilayer interaction Mamba (FMIM), which integrates inter-frame motion information into scanning prediction. This enables the network to adaptively adjust interaction priorities between shallow texture and high-level semantic features based on motion intensity, mitigating early information forgetting and enhancing robustness in dynamic scenes. Extensive experiments on four major benchmarks demonstrate that MIFMNet achieves state-of-the-art performance on precision and success rate, particularly excelling in UAV scenarios involving occlusion, scale variations, and rapid motion. Simultaneously, it achieves an inference speed of 35.3 FPS, enabling efficient deployment on resource-constrained platforms, thereby providing robust support for UAV applications of RGBT tracking.