The significant differences in target scales of remote sensing images lead to remarkable variations in visual features, posing significant challenges for feature extraction, fusion, regression, and classification. For example, models frequently struggle to capture features of targets across all scales, inadequately consider the weights and importance of features at different scales during fusion, and encounter accuracy limitations when detecting targets of varying scales. To tackle these challenges, we proposes a Scale-Robust Feature Aggregation and Diffusion Network (SRFAD-Net) for remote sensing target detection. This model includes a Scale-Robust Feature Network (SRFN), an Adaptive Feature Aggregation and Diffusion (AFAD) module, and a Focaler-GIoU Loss. SRFN extracts scale-robust features by constructing a multi-scale pyramid. It includes a downsampling (ADown) module that combines the advantages of average pooling and max pooling, effectively preserving background information and salient features. This further enhances the network’s ability to handle targets of varying scales and shapes. The introduced Deformable Attention(DAttention) mechanism captures target features effectively through adaptive adjustment of the receptive field’s shape and size, reducing background clutter and substantially enhancing the model’s performance in detecting distant objects. In the feature fusion stage, we propose the AFAD module, which utilizes a dimension-adaptive perceptual selection mechanism and parallel depthwise convolutional operations to precisely aggregate multi-channel information. It then employs a diffusion mechanism to spread contextual information across various scales, greatly improving the network’s ability to extract and fuse features across multiple scales. For the detection head, we adopt the Focaler-GIoU Loss, leveraging its advantages in handling non-overlapping bounding boxes, effectively alleviating the difficulty of localization caused by scale variations. We have undertaken experiments on two widely utilized aerial target datasets: the Remote Sensing Scene Object Detection Dataset (RSOD) and NWPU VHR-10, which is a high-resolution object detection dataset from Northwestern Polytechnical University. The findings of these experiments clearly illustrate that SRFAD-Net surpasses the performances of mainstream detectors.

Small object detection in aerial imagery remains challenging due to sparse feature representation, limited spatial resolution, and complex background interference. Current deep learning approaches enhance detection performance through multi-scale feature fusion, leveraging convolutional operations to expand the receptive field or self-attention mechanisms for global context modeling. However, these methods primarily rely on spatial-domain features, while self-attention introduces high computational costs, and conventional fusion strategies (e.g., concatenation or addition) often result in weak feature correlation or boundary misalignment. To address these challenges, we propose a unified spatial-frequency modeling and multi-scale alignment fusion framework, termed USF-DETR, for small object detection. The framework comprises three key modules: the Spatial-Frequency Interaction Backbone (SFIB), the Dual Alignment and Balance Fusion FPN (DABF-FPN), and the Efficient Attention-AIFI (EA-AIFI). The SFIB integrates the Scharr operator for spatial edge and detail extraction and FFT/IFFT for capturing frequency-domain patterns, achieving a balanced fusion of global semantics and local details. The DABF-FPN employs bidirectional geometric alignment and adaptive attention to enhance the significance expression of the target area, suppress background noise, and improve feature asymmetry across scales. The EA-AIFI streamlines the Transformer attention mechanism by removing key-value interactions and encoding query relationships via linear projections, significantly boosting inference speed and contextual modeling. Experiments on the VisDrone and TinyPerson datasets demonstrate the effectiveness of USF-DETR, achieving improvements of 2.3% and 1.4% mAP over baselines, respectively, while balancing accuracy and computational efficiency. The framework outperforms state-of-the-art methods in small object detection.

Rotated object detection is a crucial task in aerial image analysis. To address challenges such as multi-directional object rotation, complex backgrounds with occlusions, and the trade-off between speed and accuracy in remote sensing images, this paper introduces a lightweight progressive fusion calibration network for rotated object detection (LPFC-RDet). The network comprises three main modules: the Retentive Meet Transformers (RMT) feature extraction block, the Progressive Fusion Calibration module (PFC), and the Shared Group Convolution Lightweight detection head (SGCL). The RMT feature extraction block integrates a retentive mechanism with global context modeling to learn rotation-insensitive features. The PFC module employs pixel-level, local-level, and global-level weights to calibrate features, enhancing feature extraction from occluded objects while suppressing background interference. The SGCL detection head uses decoupled detection tasks and shared group convolution layers to achieve parameter sharing and feature interaction, improving accuracy while maintaining a lightweight structure. Experimental results demonstrate that our method surpasses state-of-the-art detectors on three widely used remote sensing object datasets: HRSC2016, UCAS_AOD, and DOTA.

The pursuit of accurate yet computationally efficient object detection within remote sensing imagery remains a cornerstone for the advancement of intelligent interpretation systems. Although substantial progress has been achieved in recent years, prevailing approaches still exhibit notable deficiencies in three critical aspects: the discriminative capacity of feature representation, the depth of semantic modeling, and the effectiveness of multi-scale information fusion. These shortcomings become particularly pronounced when addressing small-scale targets, which are highly susceptible to omission or misclassification. In response to these limitations, this work introduces HyperFusion-DEIM, a cascaded detection paradigm specifically designed to simultaneously reinforce object-level representations, enrich contextual semantic dependencies, and optimize scale-aware feature integration. Central to this framework is the Multi-Path Attention Network (MAPNet), which augments shallow semantic cues and edge-texture sensitivity for small object recognition through the joint operation of the Multi-Path Attention Fusion (MPAF) module and the Shallow Robust Feature Downsampling (SRFD) mechanism. Complementing this, the Scale-Aware Feature Enhancement (SAFE) encoder incorporates a Multi-level Feature Concentration (MFC) module to achieve cross-layer geometric alignment, while the integration of Transformer layers with HyperACE enables the capture of long-range semantic correlations without compromising spatial fidelity. Empirical validation conducted on the SIMD and VEDAI benchmarks demonstrates the clear superiority of HyperFusion-DEIM over state-of-the-art lightweight detectors in both predictive accuracy and robustness. Specifically, the model attains 64.5% AP on SIMD, outperforming RT-DETR and DEIM by 4.8% and 4.6%, respectively, while sustaining a peak inference throughput of 296.33 FPS. On VEDAI, HyperFusion-DEIM surpasses YOLOv12 and YOLOv13 by margins of 4.9% and 8.0%, and exceeds RT-DETRv2 and DEIM by 2.5% and 8.5%, all while maintaining real-time operation at 79.7 FPS. This performance showcases HyperFusion-DEIM's practical viability for real-time detection, particularly in resource-constrained environments where both speed and accuracy are critical.

Crop leaf disease detection plays a crucial role in ensuring healthy crop growth and improving food security. Disease features are often small and have blurry edges, while background interference is strong, making precise detection a significant challenge. Although YOLO-based methods perform well in object detection, they still struggle to effectively handle the extraction of lesion details and background noise interference when applied to crop leaf disease detection. To address these challenges, this study introduces an innovative crop leaf disease detection approach built upon the YOLO, named AG-HAF. This method proposed two modules, the additive gated convolutional unit (AGCU), which introduces a gating mechanism to dynamically adjust the importance of features, enhancing the detection of small and blurry lesions, improving nonlinear feature modeling, and suppressing irrelevant background interference. Hierarchical Attention Fusion Module (HACFM), which utilizes a hierarchical attention mechanism to optimize the fusion of multi-scale features, enhancing the representation and semantic information of disease regions, and further improving the model's adaptability to complex backgrounds. Ablation and comparative experiments show that AG-HAF outperforms existing methods across various metrics, particularly excelling in disease detecting in complex backgrounds and small lesions, demonstrating significant potential for practical applications.

Infrared and visible image fusion-based object detection is critical for robust environmental perception under adverse conditions, yet existing methods still suffer from insufficient modeling of modality discrepancies and limited adaptivity in their fusion mechanisms. This work proposes an asymmetric spatial–frequency fusion network, AsyFusionNet. The network adopts an asymmetric dual-branch backbone that extends the RGB branch to P5 while truncating the infrared branch at P4, thereby better aligning with the physical characteristics of the two modalities, enhancing feature complementarity, and enabling fine-grained modeling of modality differences. On top of this backbone, a local–global attention fusion (LGAF) module is introduced to model local and global attention in parallel and reorganize them through lightweight convolutions, achieving joint spatial–channel selective enhancement. Modality-specific feature enhancement is further realized via a hierarchical attention module (HAM) in the RGB branch, which employs dynamic kernel selection to emphasize multi-level texture details, and a fourier spatial spectral modulation (FS2M) module in the infrared branch, which more effectively captures global thermal radiation patterns. Extensive experiments on the $M^3$FD and VEDAI datasets demonstrate that AsyFusionNet attains $86.3\%$ and $54.1\%$${\mathrm{mAP}}_{50}$, respectively, surpassing the baseline by $8.8$ and $6.4$ points (approximately $11.4\%$ and $13.4\%$ relative gains) while maintaining real-time inference speed.