A prompt and precise estimation of traffic conditions on the scale of a few minutes by analyzing past data is crucial for establishing an effective intelligent traffic management system. Nevertheless, because of the irregularity and nonlinear features of traffic flow data, developing a prediction model with excellent robustness poses a significant obstacle. Therefore, we propose genetic-search-algorithm-improved kernel extreme learning machine, termed GA-KELM, to unleash the potential of improved prediction accuracy and generalization performance. By substituting the inner product with a kernel function, the accuracy of short-term traffic flow forecasting using extreme learning machines is enhanced. The genetic algorithm evades manual traversal of all possible parameters in searching for the optimal solution. The prediction performance of GA-KELM is evaluated on eleven benchmark datasets and compared with several state-of-the-art models. There are four benchmark datasets from the A1, A2, A4, and A8 highways near the ring road of Amsterdam, and the others are D1, D2, D3, D4, D5, D6, and P, close to Heathrow airport on the M25 expressway. On A1, A2, A4, and A8, the RMSEs of the GA-KELM model are 284.67 vehs/h, 193.83 vehs/h, 220.89 vehs/h, and 163.02 vehs/h, respectively, while the MAPEs of the GA-KELM model are 11.67%, 9.83%, 11.31%, and 12.59%, respectively. The results illustrate that the GA-KELM model is obviously superior to state-of-the-art models.

Infrared small target detection remains challenging because small targets, cloud edges, sea reflections, building heat sources, and sensor noise often share similar high-frequency responses. Existing local enhancement methods tend to amplify target-like clutter, whereas global modeling methods may dilute sparse target details. To address this issue, this paper proposes CFGMNet (Cross-Frequency Context-Guided Mamba Network), which reformulates infrared small target detection as a high-frequency candidate verification problem constrained by low-frequency background context. Specifically, the proposed CFG-Mamba module decomposes deep features into low-frequency background components and high-frequency candidate regions. Mamba is applied only to the low-frequency branch to capture long-range background dependencies, and the resulting contextual representation is used to gate high-frequency responses, thereby suppressing target-like clutter without indiscriminately enhancing local details. Furthermore, a local contrast gate, a residual attention decoder, and a decoupled prediction head are introduced to perform local saliency calibration, skip-connection noise filtering, and response verification. Experiments on NUAA-SIRST, NUDT-SIRST, and IRSTD-1K demonstrate that CFGMNet achieves a favorable balance between segmentation accuracy, false alarm suppression, and inference efficiency. In particular, CFGMNet achieves 85.27% mIoU and 93.77% F1 on NUAA-SIRST and obtains the lowest false alarm rate on IRSTD-1K, while reaching 141 FPS under forward-pass-only evaluation on an RTX 4090 GPU.

Infrared–visible image fusion aims to integrate thermal target information from infrared images and structural texture information from visible images into a single informative image. Existing deep fusion methods still face challenges in preserving fine textures, maintaining structural consistency, and balancing complementary information under low-light conditions. To address these issues, this paper proposes MRCDFusion, an unsupervised infrared–visible image fusion network based on residual conditional diffusion and Transformer refinement. Specifically, a shared dense encoder is used to extract modality-specific and cross-modal complementary features from infrared and visible images. A Modality-Level Attention Module (MLAM) is then introduced to aggregate strong responses from infrared and visible features and construct modality-aware condition features for guiding the diffusion process. Instead of generating fused features from scratch, the proposed method adopts a base-plus-residual diffusion strategy, in which base features preserve global structures and residual diffusion enhances local details. A deterministic noise strategy is further introduced to improve inference reproducibility. The diffusion-enhanced features are refined by a window Transformer and depthwise separable convolutions, followed by gated feature fusion and progressive image reconstruction. Experiments are conducted primarily on the low-light LLVIP dataset, while FMB, TNO, and RoadScene are used for zero-shot cross-dataset evaluation without additional fine-tuning. The results show that MRCDFusion achieves particularly strong performance in gradient- and edge-related metrics while remaining competitive in visual information fidelity and cross-modal correlation metrics. Ablation studies verify the effectiveness of the main components, and downstream detection and auxiliary segmentation experiments further demonstrate the potential utility of the fused representations for subsequent visual perception tasks.

Infrared and visible image fusion aims to integrate the complementary information from heterogeneous sensors, thereby enhancing the robustness of visual perception in complex environments. Most existing methods, however, employ fixed network parameters, a characteristic that limits their adaptive modeling capabilities for cross-modal information under scenarios such as drastic illumination changes, low light conditions, dense fog, and strong glare. To address this issue, we propose a scene-adaptive image fusion network, termed HL-Fuse, based on dynamic low-rank modulation and frequency-domain collaboration. For the spatial domain, the Hyper-LoRA is introduced via our designed SceneHyperNet, which mathematically constrains parameter variations within a low-rank subspace to adaptively calibrate attention mappings according to the global scene information. For the frequency domain, a tailored FAM is introduced to bridge spatial-domain feature aggregation and explicit spectrum reweighting by implementing targeted high- and low-frequency filtering, thereby enhancing edge and texture representation. Experiments conducted on the MSRS, TNO, M³FD, and FMB datasets demonstrate that HL-Fuse achieves competitive performance in terms of both multiple objective metrics and subjective visual quality, while the overall performance in the MSRS downstream object detection task is also enhanced. These results indicate the potential value of HL-Fuse for complex scene perception and remote sensing applications.

Multimodal image fusion aims to integrate complementary information from heterogeneous imaging modalities into a single informative image. However, many deep learning-based fusion methods rely on complex feature extractors, leading to high computational cost and limited suitability for real-time deployment on resource-constrained devices. To address this issue, this paper proposes a lightweight Mamba-INN dual-branch network for efficient multimodal image fusion. The proposed model decouples global structure modeling from local detail preservation. A simplified Mamba-inspired branch is designed to capture long-range contextual dependencies, while a lightweight invertible neural network branch preserves high-frequency textures and edge information through information-preserving transformations. The lightweight INN branch preserves high-frequency texture and edge information during the forward feature transformation process through reversible feature partitioning, coupled transformations, and exponential scale modulation, thereby reducing the loss of detail caused by feature compression. Compact shallow feature refinement, module reuse, low-dimensional channel design, and a streamlined decoder are further introduced to reduce redundant computation. Experiments on infrared-visible and medical image fusion benchmarks, including MSRS, TNO, RoadScene, MRI-CT, MRI-PET, and MRI-SPECT datasets, demonstrate that the proposed method achieves competitive fusion quality with low model complexity. The proposed method achieves performance comparable to or better than that of methods such as CDDFuse, U2Fusion, CNN and SDNet on metrics including MI, VIF, Qabf, and SSIM for infrared-visible and medical image fusion tasks, while containing only 0.24 million parameters and requiring 24.04 GFLOPs of computational power at an input resolution of 256 × 256. Compared to CDDFuse, our method significantly reduces model complexity, enhancing the potential for lightweight deployment while maintaining fusion quality.

To address the challenges of extremely small object scales, weak texture information, and severe background interference in aerial remote sensing images, we propose MambaIR-YOLO, a feature-guided lightweight state-space framework for aerial small-object detection. Based on the YOLOv5 architecture, this method introduces systematic improvements in three key areas—fine-grained feature modeling, long-range dependency learning, and high-resolution spatial information preservation—while ensuring real-time performance. Specifically, a feature-level MambaIR_SR (feature-level Mamba-based super-resolution guidance) training auxiliary branch is designed to generate high-resolution detail-guided information by fusing shallow-level detail features with deep-level semantic features, improving the representation of small-object edges and textures during the training phase. In the main network, we introduce the Object Detail State-Space Block (ODSSBlock), driven by Lightweight Mamba. Through a channel-compressed state-space modeling mechanism, the ODSSBlock unifies local detail preservation and long-range context modeling with low computational overhead. Concurrently, a Feature Modulation Block (FMB) is constructed at the shallow feature level to enhance the representation of high-frequency structural information, thereby mitigating the irreversible detail degradation caused by multiple downsampling steps. In the feature fusion and detection stages, we introduce a Coordinate and Channel Attention (C3CA) attention enhancement module and construct a lightweight decoupled detection head based on a Lightweight Decoupled Head (LADH). This performs single-scale dense prediction on high-resolution features, improving small-object localization and reducing information loss. Notably, the MambaIR_SR branch is exclusively utilized for optimization during the training phase and is entirely discarded during inference, introducing no additional computational overhead. Experimental results on the VEDAI dataset demonstrate that the proposed method achieves an average mAP50 of 84.19 ± 0.03% over three independent runs, with only 4.46 M parameters and 19.97 GFLOPs, outperforming the baseline methods while maintaining low computational cost.