Issues such as insufficient feature representation and background confusion make detection tasks for small object in remote sensing arduous. Particularly when the algorithm will be deployed on board for real-time processing, which requires extensive optimization of accuracy and speed under limited computing resources. To tackle these problems, an efficient detector called FFCA-YOLO(Feature enhancement, Fusion and Context Aware YOLO) is proposed in this paper. FFCA-YOLO includes three innovative lightweight and plug-and-play modules: feature enhancement module(FEM), feature fusion module(FFM) and spatial context aware module(SCAM). These three modules improve the network capabilities of local area awareness, multi-scale feature fusion and global association cross channels and space, respectively, while trying to avoid increasing complexity as possible. Thus the weak feature representations of small objects are enhanced and the confusable backgrounds are suppressed. Two public remote sensing datasets(VEDAI and AI-TOD) for small object detection and one self-built dataset(USOD) are used to validate the effectiveness of FFCA-YOLO. The accuracy of FFCA-YOLO reaches 0.748, 0.617 and 0.909(in terms of mAP ₅₀ ) that exceeds several benchmark models and state-of-the-art methods. Meanwhile, the robustness of FFCA-YOLO is also validated under different simulated degradation conditions. Moreover, to further reduce computational resource consumption while ensuring efficiency, a lite version of FFCA-YOLO(L-FFCA-YOLO) is optimized by reconstructing the backbone and neck of FFCA-YOLO based on partial convolution. L-FFCA-YOLO has faster speed, smaller parameter scale, lower computing power requirement but little accuracy loss compared with FFCA-YOLO. The source code will be available at https://github.com/yemu1138178251/FFCA-YOLO.

Compared with anchor-based detectors, anchor-free detectors have the advantage of flexibility and a lower calculation complexity. However, in complex remote sensing scenes, the limited geometric size, weak features of objects, and widely distributed environmental elements similar to the characteristics of objects make small object detection a challenging task. To solve these issues, we propose an anchor-free detector named FE-CenterNet, which can accurately detect small objects such as vehicles in complicated remote sensing scenes. First, we designed a feature enhancement module (FEM) composed of a feature aggregation structure (FAS) and an attention generation structure (AGS). This module contributes to suppressing the interference of false alarms in the scene by mining multiscale contextual information and combining a coordinate attention mechanism, thus improving the perception of small objects. Meanwhile, to meet the high positioning accuracy requirements of small objects, we proposed a new loss function without extra calculation and time cost during the inference process. Finally, to verify the algorithm performance and provide a foundation for subsequent research, we established a dim and small vehicle dataset (DSVD) containing various objects and complex scenes. The experiment results demonstrate that the proposed method performs better than mainstream object detectors. Specifically, the average precision (AP) metric of our method is 7.2% higher than that of the original CenterNet with only a decrease of 1.3 FPS.

In remote sensing images, vehicles often appear on a minuscule scale, lacking features and easily overwhelmed by intricate background information. This becomes even more challenging in low illumination or occluded environments, leading to missed detections and false alarms. A novel vehicle detection algorithm, known as Local and Global Aware YOLO (LGA-YOLO), is introduced to tackle these issues. LGA-YOLO incorporates two innovative and plug-and-play modules: the Multi-Scale Large Kernel Local Aware Module (MLKM) and the Directional Global Context Aware Module (DGAM). MLKM widens the receptive field and enhances local features, while DGAM gathers global context information, highlighting vehicle features against complex backgrounds. Based on these modules, a high-low feature fusion network is reconstructed, capturing multi-scale object features and effectively leveraging shallow features. Our self-constructed dataset (USOD), VEDAI, and DOTA are employed to validate LGA-YOLO's efficacy. In USOD, the results demonstrate the remarkable performance of LGA-YOLO, with precision, recall, AP0.5, and AP0.5:0.95 scores of 0.927, 0.889, 0.930, and 0.371, respectively. In VEDAI and DOTA, the mAP0.5 of LGA-YOLO reaches 0.803 and 0.781, respectively. These metrics not only surpass baseline models but also leading-edge algorithms in the field.

Multimodal remote sensing images provide complementary information, enhancing the effectiveness of object detection tasks in open-world scenarios. To address the imbalance of information richness between modalities in multimodal object detection, we propose a simple yet effective multi-source image object detection method (DKDNet). Our contributions are twofold: (a) we introduce diffusion deformation convolution (DDConv), which combines deformation convolution with adaptive long-range receptive fields to further enhance the ability to perceive object pose variations and capture distant information. (b) We propose the bidirectional feature distillation and information complementary fusion network (BDFusion), where different modalities exchange information through a knowledge distillation strategy, explicitly enhancing the information interaction between modalities. Finally, we adaptively build spatial domain complementarity between different modalities via self-correction, revealing implicit correlations. Experimental results on the publicly available vehicle detection in aerial imagery (VEDAI) dataset and the optical and SAR ship detection dataset (OSSDD), collected in the Suez Canal region, demonstrate that our proposed method achieves superior performance with acceptable inference time, making it suitable for various realworld scenarios.

Remote sensing (RS) images often suffer from spatial resolution degradation and edge blurring due to sensor limitations and complex degradation factors such as atmospheric turbulence and motion. These issues negatively impact subsequent tasks like classification and detection. Most existing super-resolution (SR) methods rely on synthetic low-resolution(LR) data generated by bicubic interpolation, which fails to reflect real degradation processes, resulting in limited performance in practical applications. Furthermore, many methods insufficiently exploit high-frequency details and lack the ability to effectively model both global context and local structures. To address these challenges, this paper proposes an edge-enhanced SR framework for remote sensing images. A more realistic degradation model is constructed by estimating blur kernels from real LR images. The Edge-Enhanced and Structure-Aware Generative Adversarial Network (EESAGAN) is designed, incorporating the edge prior enhancement module (EPE), the edge-aware local attention module(ELAM), and the dynamic feature space attention module(DFSA). The edge-guided loss is also introduced to enhance image sharpness and perceptual consistency. Experimental results demonstrate that the proposed method achieves superior performance in PSNR, SSIM, and ERGAS compared to existing SR approaches.