The “low, slow, and small” target (LSST) poses a significant threat to the military ground unit. It is hard to defend against due to its invisibility to numerous detecting devices. With the onboard deep learning-based object detection methods, the intelligent LSST (ILSST) can find and detect the ground unit autonomously in a denied environment. This paper proposes an adversarial patch-based defending method to blind the ILSST by attacking its onboard object detection network. First, an adversarial influence score was established to indicate the influence of the adversarial noise on the objects. Then, based on this score, we used the least squares algorithm and Bisectional search methods to search the patch’s optimal coordinates and size. Using the optimal coordinates and size, an adaptive patch-generating network was constructed to automatically generate patches on ground units and hide the ground units from the deep learning-based object detection network. To evaluate the efficiency of our algorithm, a new LSST view dataset was collected, and extensive attacking experiments are carried out on this dataset. The results demonstrate that our algorithm can effectively attack the object detection networks, is better than state-of-the-art adversarial patch-generating algorithms in hiding the ground units from the object detection networks, and has high transferability among the object detection networks.

With the ongoing development of image technology, the deployment of various intelligent applications on embedded devices has attracted increased attention in the industry. One such application is automatic image captioning for infrared images, which involves converting images into text. This practical task is widely used in night security, as well as for understanding night scenes and other scenarios. However, due to the differences in image features and the complexity of semantic information, generating captions for infrared images remains a challenging task. From the perspective of deployment and application, to improve the correlation between descriptions and objects, we introduced the YOLOv6 and LSTM as encoder-decoder structure and proposed infrared image caption based on object-oriented attention. Firstly, to improve the domain adaptability of the detector, we optimized the pseudo-label learning process. Secondly, we proposed the object-oriented attention method to address the alignment problem between complex semantic information and embedded words. This method helps select the most crucial features of the object region and guides the caption model in generating words that are more relevant to the object. Our methods have shown good performance on the infrared image and can produce words explicitly associated with the object regions located by the detector. The robustness and effectiveness of the proposed methods were demonstrated through evaluation on various datasets, along with other state-of-the-art methods. Our approach achieved BLUE-4 scores of 31.6 and 41.2 on KAIST and Infrared City and Town datasets, respectively. Our approach provides a feasible solution for the deployment of embedded devices in industrial applications.

The widespread application of convolutional neural networks (CNNs) has led to significant advancements in object detection. However, challenges remain in achieving efficient and precise extraction of critical features when applying typical CNN-based methods to remote sensing detection tasks: (1) The convolutional kernels sliding horizontally in the backbone are misaligned with the features of arbitrarily oriented objects. Additionally, the detector shares the features extracted from the backbone, but the classification task requires orientation-invariant features while the regression task requires orientation-sensitive features. The inconsistency in feature requirements makes it difficult for the detector to extract the critical features required for each task. (2) The use of deeper convolutional structures can improve the detection accuracy, but it also results in substantial convolutional computations and feature redundancy, leading to inefficient feature extraction. To address this issue, we propose a Task-Sensitive Efficient Feature Extraction Network (TFE-Net). Specifically, we propose a special mixed fast convolution module for constructing an efficient network architecture that employs cheap transform operations to replace some of the convolution operations, generating more features with fewer parameters and computation resources. Next, we introduce the task-sensitive detection module, which first aligns the convolutional features with the targets using adaptive dynamic convolution based on the orientation of the targets. The task-sensitive feature decoupling mechanism is further designed to extract orientation-sensitive features and orientation-invariant features from the aligned features and feed them into the regression and classification branches, respectively, which provide the critical features needed for different tasks, thus improving the detection performance comprehensively. In addition, in order to make the training process more stable, we propose a balanced loss function to balance the gradients generated by different samples. Extensive experiments demonstrate that our proposed TFE-Net can achieve superior performance and obtain an effective balance between detection speed and accuracy on DOTA, UCAS-AOD, and HRSC2016.

Drones are increasingly being used in maritime engineering for ship maintenance, emergency rescue, and safety monitoring tasks. In these tasks, action recognition is important for human–drone interaction and for detecting abnormal situations such as falls or distress signals. However, the maritime environment is highly challenging, with illumination variations, water spray, and dynamic backgrounds often leading to ambiguity between similar actions. To address this issue, we propose MSG-GCN, a multi-semantic guided graph convolutional network for human action recognition. Specifically, MSG-GCN integrates structured prior semantic information and further introduces a textual–semantic alignment mechanism to improve the consistency and expressiveness of multimodal features. Benefiting from its lightweight hierarchical design, our model offers excellent deployment flexibility, making it well suited for resource-constrained UAV applications. Experimental results on large-scale benchmark datasets, including NTU60, NTU120 and UAV-human, demonstrate that MSG-GCN surpasses state-of-the-art methods in both classification accuracy and computational efficiency.