Existing anchor-based rotated object detection methods have achieved some amazing results, but these methods require some manual preset anchors, which not only introduce additional hyperparameters but also introduce extra computational burdens. Due to the above drawbacks, anchor-free methods have been rapidly developed in recent years. However, the existing high-performance anchor-free rotated object detection methods are relatively complex and the inference speed is also slow. And you only look once detector (YOLO) series models not only maintain high-efficiency inference but also keep competitive performance detection performance in the general object detection tasks. Hence, we propose an anchor-free rotated detector based on the exceeding you only look once detector (YOLOX) method for object detection in aerial images. Our methods consist of two improvements: a refined rotated module (RRM) and a new assigner method which is called the Gaussian distribution sampling optimal transport assignment (GSOTA) method. The RRM can align features and get more useful priors for final detector heads. The GSOTA uses Gaussian distribution to model the oriented bounding box (OBB) first, and a Gaussian center sampling (GCS) method with maximum classification center mean (MCCM) is proposed to simplify the label Assignment optimal transport (OT) problem, finally using an improved dynamic top- $k$ strategy to get an approximate solution. Extensive experiments demonstrate that our models can achieve competitive performance in several challenging aerial object detection datasets while keeping the best efficiency. Our R2YOLOX-X model achieves 79.33%, 97.4%, 97.7%, and 92.5% mean of Average Precision (mAP) on the dataset for object detection in aerial image (DOTA), HRSC2016, University of Chinese Academy of Science (UCAS)-dataset of object detection in aerial image (AOD), and FGSD2021, respectively, while R2YOLOX-S can reach the fastest 58.2 FPS when inferencing on aerial datasets and R2YOLOX-L gets the best speed-accuracy trade-off.

Remote sensing change detection (RSCD) aims to utilize paired temporal remote sensing images to detect surface changes in the same area. Traditional CNN-based methods are limited by the size of the receptive field, making it difficult to capture the global features of remote sensing images. In contrast, Transformer-based methods address this issue with their powerful modeling capabilities. However, applying the Transformer architecture to image processing introduces a quadratic complexity problem, significantly increasing computational costs. Recently, the Mamba architecture based on state-space models has gained widespread application in the field of RSCD due to its excellent global feature extraction capabilities and linear complexity characteristics. Nevertheless, existing Mamba-based methods lack optimization for complex change areas, making it easy to lose shallow features or local features, which leads to poor performance on challenging detection cases and high-difficulty datasets. In this paper, we propose a Mamba-based RSCD network for difficult cases (DC-Mamba), which effectively improves the model’s detection capability in complex change areas. Specifically, we introduce the edge-feature enhancement (EFE) block and the dual-flow state-space (DFSS) block, which enhance the details of change edges and local features while maintaining the model’s global feature extraction capability. We propose a dynamic loss function to address the issue of sample imbalance, giving more attention to difficult samples during training. Extensive experiments on three change detection datasets demonstrate that our proposed DC-Mamba outperforms existing state-of-the-art methods overall and exhibits significant performance improvements in detecting difficult cases.

The degradation of image resolution reduces the detection performance in aerial imagery because it generates a large number of small objects, and accurately detecting these small objects remains a challenge. Existing methods mostly use a Super-Resolution (SR) model to first obtain the SR image of the Low-Resolution degraded image ( $I^{LR}$ ) and then use this image as the input of the object detection (OD) network to solve this problem. However, this architecture that involves executing a complex SR network before the detector is time-consuming and makes it hard to achieve real-time model inference. To address this challenge, we propose a simple and effective rotated small object detection method, named End-to-end Super-Resolution enhanced real-Time rotated object Detector (ESRTMDet). Firstly, we design a lightweight Embedded feature map Super-Resolution Module (ESRM) embedded in the detection model to enhance and amplify the backbone output features, making the detection heads detect small objects more easily. Furthermore, we train a parallel SR network branch (PSRB) simultaneously that uses the backbone feature to restore a High-Resolution image ( $I^{HR}$ ). Through our proposed feature alignment loss ( $\mathcal {L}_{AL}$ ) and feature affinity layer (FAL), our PSRB effectively guides the feature map enhancement of ESRM. Finally, through end-to-end joint optimization of the detector and PSRB, the detection performance on $I^{LR}$ is significantly improved. Extensive experiments over DOTA and UCAS-AOD demonstrate that our method can achieve state-of-the-art (SOTA) results. Additionally, we discard our PSRB and use $I^{LR}$ as the input during inference, reducing the inference time-consuming of our model. Therefore, our ESRTMDet-X not only achieves 77.11% mean of Average Precision (mAP) on the degraded DOTA dataset, but also achieves an amazing inference speed of 337 FPS, thus obtaining the best speed-accuracy trade-off. The code and models will be released at https://github.com/liufeinuaa/aisodet.git .

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs’ network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs’ nodes.

Traditional Euclidean spatial data processing is difficult to capture the inherent relationships of unstructured data such as bearing vibration signals. Representing vibration signals in graphical form helps to preserve their topological structure and temporal information. Secondly, most existing graph convolutional network methods are based on large graph structured data, which incurs certain memory overhead when aggregating high-order neighborhood node information and ignores important information between samples in the global graph structure. To address these issues, this paper proposes a high-order multi-head graph attention network based on an adaptive small graph structure (ASG-HOMGAT) for fault diagnosis of rolling bearings. Firstly, the adaptive preprocessing layer is used to adaptively denoise and compress the one-dimensional time-domain vibration signal, generating small rule graph data with topological structure. Then, these small graph structured data samples are input into a higher-order graph neural network, which aggregates features from multiple higher-order neighborhoods to achieve richer feature representations and fully explore the intrinsic correlation between samples. Finally, these features are aggregated into a reinforced representation of graph nodes through a multi head attention mechanism, and a SoftMax classifier is used for fault classification. The experimental results show that the ASG-HOMGAT method has better performance compared to mainstream graph neural network diagnostic models. The code and model will be released at: https://github.com/ding-ss/ASG-HOMGAT.