In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field.

In the case of a boom in space resource development, space debris will increase dramatically and cause serious problems for the spacecraft in orbit. To address this problem, a novel context sensing-YOLOv5 (CS-YOLOv5) is proposed for small and weak space object detection, which could realize the extraction of local context information and the enhancement and fusion of spatial information. To enhance the expression ability of feature information and the identification ability of the network, we propose the cross-layer context fusion module (CCFM) through multiple branches in parallel to learn the context information of different scales. At the same time, to map the small-scale features sequentially to the features of the previous layer, we design the adaptive weighting module (AWM) to assist the CCFM in further enhancing the expression of features. Additionally, to solve the problem that the spatial information of small objects is easily lost, we designed the spatial information enhancement module (SIEM) to adaptively learn the weak spatial information of small objects that need to be protected. To further enhance the generalization ability of CS-YOLOv5, we propose a contrast mosaic data augmentation to enrich the diversity of the sample. Extensive experiments are conducted on self-built datasets, which strongly prove the effectiveness of our method in space object detection.

Drone object detection in low-altitude airspace plays an essential role in many practical applications, such as security and airspace monitoring. Despite the remarkable progress made by many methods, drone object detection still remains challenging due to the complex background and huge differences in scales of drones. To address the above issues, an improved fully convolutional one-stage object detection (FCOS) model based on adaptive weighted feature fusion (AWFF) module is proposed for multiscale drone object detection in complex background. By learning the spatial relevance of feature maps at each scale and improving the scale invariance of features based on the channel attention mechanism, AWFF module could adaptively fuse the features of adjacent scale. In addition, a receptive field enhancement module is designed to reduce the information loss in the feature fusion process. Extensive experiments are conducted to evaluate the effectiveness of the proposed module and method on the constructed low-altitude drone dataset, which concludes that the mean average precision of the AWFF-FCOS is increased by 2.1% compared with the baseline method. And extensive ablation experiments further demonstrate that the proposed AWFF module and REF module could be integrated into the state-of-the-art method to improve the performance of drone object detection.

The fusion of synthetic aperture radar (SAR) and visible images offers complementary spatial and spectral information, enabling more reliable and comprehensive scene interpretation. However, SAR speckle noise and the intrinsic modality gap pose significant challenges for existing methods in extracting consistent and complementary features. To address these issues, we propose VGSRF-Net, a Retinex-guided SAR reconstruction-driven fusion network that leverages visible-image priors to refine SAR features. This approach effectively reduces modality discrepancies before fusion, enabling improved multi-modal representation. The cross-modality reconstruction module (CMRM) reconstructs SAR features guided by visible priors, effectively reducing modality discrepancies before fusion and enabling improved multi-modal representation. The multi-modal feature joint representation module (MFJRM) enhances cross-modal complementarity by integrating global contextual interactions and local dynamic convolution, thereby achieving further feature alignment. Finally, the feature enhancement module (FEM) refines multi-scale spatial features and selectively enhances high-frequency details in the frequency domain, improving structural clarity and texture fidelity. Extensive experiments on diverse real-world remote sensing datasets demonstrate that VGSRF-Net surpasses state-of-the-art methods in denoising, structural preservation, and generalization under varying noise and illumination conditions.

Lens flare significantly affects the visual quality of images, and it can prevent a computer vision program from performing well in certain tasks, such as optical flow estimation and stereo matching. Removing this issue can help ensure that the program performs optimally in future. Existing deep learning-based methods for lens flare removal suffer from the problem of partial loss of image details and structural information due to the lack of information exchange between channels and weak utilization of local contextual details in the images. To address this issue, we propose a network for nighttime lens flare removal by combining channel attention mechanism and depthwise over-parameterized convolution. Firstly, to enhance the perception and extraction of different types and directions of lens flare, we incorporate the channel attention mechanism into the attention component of the Transformer module, which strengthens the interaction between channels and enhances the feature maps of the input through channel feature enhancement. Secondly, to improve the resolution ability of lens flare details and the effectiveness of lens flare removal, we introduce a depth over-parameterized feedforward network by adding over-parameterized depth convolution blocks in the feedforward network, which enhances the utilization of local contextual information in the images. Experimental results on the Flare7k++ dataset demonstrate that our network achieves a 0.049 dB improvement in PSNR and a slight reduction of 0.0008 in LPIPS compared to the optimal method Uformer, a 0.002 improvement in SSIM compared to the optimal method Restormer.