Airplane detection in remote sensing images remains a challenging problem due to the complexity of backgrounds. In recent years, with the development of deep learning, object detection has also obtained great breakthroughs. For object detection tasks in natural images, such as the PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) VOC (Visual Object Classes) Challenge, the major trend of current development is to use a large amount of labeled classification data to pre-train the deep neural network as a base network, and then use a small amount of annotated detection data to fine-tune the network for detection. In this paper, we use object detection technology based on deep learning for airplane detection in remote sensing images. In addition to using some characteristics of remote sensing images, some new data augmentation techniques have been proposed. We also use transfer learning and adopt a single deep convolutional neural network and limited training samples to implement end-to-end trainable airplane detection. Classification and positioning are no longer divided into multistage tasks; end-to-end detection attempts to combine them for optimization, which ensures an optimal solution for the final stage. In our experiment, we use remote sensing images of airports collected from Google Earth. The experimental results show that the proposed algorithm is highly accurate and meaningful for remote sensing object detection.

Instance segmentation in remote sensing images (RSI) poses significant challenges due to the diverse scales of targets, scene complexity, and a high number of targets, making most methods struggle with suboptimal performance and time-consuming computations. To solve those problems, a fast and accurate RSI instance segmentation model (named DCTC) is designed in this article. DCTC transforms classification problem into regression problem to improve the reference speed. DCTC contains two parallel branches. The contour branch performs iterative regression on contours, extracting precise contour information to improve boundary accuracy. Meanwhile, the discrete cosine transformation (DCT) branch refines mask predictions and supplements instance context information, which particularly benefits the segmentation of small targets. DCT encoding is employed in the DCT branch to convert the mask representation into DCT format, aligning the outputs of the contour and DCT branches. Three innovative modules are introduced in the DCT branch: the coarse result generation (CRG) module, iteratively deform and regression (IDR) module, and contour and DCT fusion module (CDF). The CRG module generates coarse DCT vectors and contour coordinates, facilitating information exchange between the contour and DCT branches. The IDR module iteratively refines DCT vectors, enabling DCTC to focus more on small targets and instance details. The CDF module merges DCT vectors and contour coordinates, ensuring effective interaction between boundary and context information, thereby enhancing performance. Extensive experiments demonstrate the superiority of DCTC, which achieves 67.7, 36.3, 67.4, and 55.1AP on NWPU VHR-10, iSAID, synthetic aperture radar (SAR) ship detection dataset, and high-resolution SAR images dataset, respectively, and ranks first among state-of-the-art methods while maintaining real-time processing capability. Furthermore, DCTC exhibits strong performance on both optical and SAR images, and the designed DCT branch can be simply plug into any contour-based method to improve the network performance.

In this paper, we propose a new visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm. With the tightly coupled sensor fusion of a global shutter monocular camera and a low-cost Inertial Measurement Unit (IMU), this algorithm is able to achieve robust and real-time estimates of the sensor poses in unknown environment. To address the real-time visual-inertial fusion problem, we present a parallel framework with a novel IMU initialization method. Our algorithm also benefits from the novel IMU factor, the continuous preintegration method, the vision factor of directional error, the separability trick and the robust initialization criterion which can efficiently output reliable estimates in real-time on modern Central Processing Unit (CPU). Tremendous experiments also validate the proposed algorithm and prove it is comparable to the state-of-art method.

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

When infrared small-dim target images under strong background clutters are employed to train a deep learning (DL)-based detection network, the model becomes biased toward the clutters, negatively impacting detection performance. While background estimation is able to address this issue, most convolutional neural network (CNN)-based ways require manual foreground mask extraction during the learning phases. In unsupervised tactics, it is often assumed that the background in frames is captured by a stationary camera. Howbeit, lots of small target sequences have dynamic backgrounds due to motion in the imaging platform, challenging this hypothesis. There is limited focus on unsupervised background estimation for small target images with sensor motion. To address this gap, a learning-based model, named background estimation for multiframe small-dim target detection (BEmST), is raised. BEmST combines a variational autoencoder with stable principal component pursuit (SPCP) optimization for unsupervised deep background modeling. Target detection is then performed using U-net++ on differences between the modeled background and input images. This innovative tactic integrates unsupervised probabilistic background estimation with supervised dense classification for bettered small target detection. Extensive qualitative/quantitative experiments on public datasets validate that BEmST not only outperforms state-of-the-art tactics in availably and robustly detecting small-dim target images across various challenging scenarios, but also achieves superior detection performance, such as higher probabilities of detection, lower false alarm rates, and larger areas under receiver operating characteristic (ROC) curves. The results pave a way for the future utilization of small-dim target images in a more efficient manner.

Fine-grained object detection, capable of identifying subcategories or types, is thriving in remote sensing scenes. In practice, most existing fine-grained detectors are derived from the two-stage RCNN paradigm with intricate anchor boxes, focusing on refining features of region-of-interests to boost performance, often incurs a redundant process. By contrast, the one-stage, anchor-free paradigm possesses a simple yet effective pipeline, but its exploration in fine-grained detections is still far from sufficient. In this paper, we propose a one-stage, anchor-free fine-grained detector for remote sensing aircraft recognition. We initially delve into predominant issues when extending the one-stage framework to conduct fine-grained detections, typified by severe inter-class confusion and inferior performance in rare categories. Then, we design a fine-grained classification branch, including a Region-to-Region Context Distributor (R2CD), a Class-aware Decoupled Focal Loss (CDFL), and a Cross-Shaped Sample Space (CS3), to address these hindrances. Specifically, the R2CD flexibly integrates the sparse attention mechanism with mask prediction operations to conduct region-level content interactions separately within the foreground and background of feature maps, significantly alleviating the inter-class confusion by enhancing subtle features; the CDFL employs dynamic modulation factors driven by optimization gradients to regulate loss contributions across categories while optimizing category-specific heatmaps, thus prioritizing rare categories with hard samples; the CS3 attains a preferable assignment strategy of positive and negative samples by incorporating structure prior, facilitating the capture of foreground features. Extensive experiments conducted on the MAR20 and FAIRPlane11 datasets demonstrate that our model excels at distinguishing fine-grained categories and is well-suited for performing fine-grained detection tasks.