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.

Cloud detection is one of the essential procedures in optical remote sensing image processing because clouds are widely distributed in remote sensing images and cause a lot of challenges, such as climate research and object detection. In this article, a lightweight deep-learning-based framework is proposed to detect cloud in remote sensing imagery. First, a multiple features fusion strategy is designed to extract learnable manual features and convolution features from visible and near-infrared bands. Then, a lightweight fully convolutional neural network (ClouDet) with a microarchitecture named dilated separable convolutional module is used to extract the multiscale contextual information and gradually recovers segmentation results with the same size as input image, which is more effective for large-scale cloud detection with larger receptive field, less parameters, and lower compute complexity. Third, context pooling is designed to amend the possible misjudgments. Visual and quantitative comparison experiments are conducted on several public cloud detection datasets, which indicates that our proposed method can accurately detect clouds under different conditions, which is more effective and accurate than the compared state-of-the-art methods.

<!-- *** Custom HTML *** --> In recent years, there has been an increasing interest in object detection in remote sensing imagery with onboard sensors and embedded platform based on deep convolutional neural networks. However, the limited cost, power consumption, compute complexity, and parameter size make the task challenging. The current object detection frameworks are mainly designed on the basis of graphics processing units (GPUs) and require further optimization in power consumption, and calculating quantity and parameter size. To address these issues, we propose an effective single-shot multibox detector (DF-SSD) framework, using the DepthFire module we designed to reform SqueezeNet as the backbone network to reduce the calculating quantity and improve the processing efficiency. To evaluate the effectiveness and superiority of DF-SSD, compared with the other state-of-the-art methods, extensive experiments are conducted on various hardware platforms, including GPU 1080ti, central processing unit i7-7700k, NVidia Jetson TX2, and Cambricon-1H8. Experiment results show that the designed algorithm can achieve a mean average precision of 75.2% on NWPU VHR-10 dataset, with 181, 5.2, 26.3, and 15 frames per second on the above four typical hardware platforms, respectively, which finally demonstrate the effectiveness and high accuracy of the proposed algorithm.

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.