To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings. Specifically, a new hybrid attention mechanism module is exploited to conduct more discriminative feature representation and is incorporated into a Siamese network. At the same time, the attention-based Siamese network fuses multilevel features for accurately tracking the target. We further introduce a hierarchical discriminator for checking the reliability of targeting, and a discriminator-based redetection network is utilized for correcting tracking failures. To effectively catch up with the appearance changes of UAVs, a template updating strategy is developed in long-term tracking tasks. Our model surpasses many state-of-the-art models on the anti-UAV benchmark. In particular, the proposed method can achieve 13.7% and 16.5% improvements in success rate and precision rate, respectively, compared with the strong baseline SiamRPN++.

Structural health monitoring (SHM) technology is of great importance to ensure the long-term and reliable operation of engineering structures. The monitoring systems for large structures are characterized by large numbers of measurement points and complex signal. In recent years, deep learning has been widely used in many fields, such as intelligent fault diagnosis and health monitoring due to its excellent feature extraction and nonlinear fitting ability. However, the existing deep networks for condition monitoring have some drawbacks. First, the multisensor information fusion is rarely considered, which is quite common in SHM systems. Second, most of the proposed models by current research are with large parameters and high complexity. To solve the problems mentioned above, we propose a lightweight SHM framework suitable for the multisensor monitoring tasks. In other words, the adaptive multisensor fusion network (ASF-Net) is proposed to dynamically mix with multichannel input samples and extract the efficient feature on different scales. Moreover, a joint loss function is established for network compression by transferring the knowledge from bulky teacher network to lightweight student network. Finally, to reduce the performance degradation during model compression, a multigeneration knowledge distillation (MGKD) strategy is proposed. The experiments on two SHM datasets show that the proposed method can achieve excellent classification accuracy, and model performance degradation of less than 2% can be obtained with at most 40.24 times less parameters and 128.82 times less computation, which has a great prospect on resource-constrained platforms.

The widespread application of Unmanned Aerial Vehicles (UAVs) presents substantial challenges to public safety, thereby necessitating accurate visual tracking for effective counter-UAV operations. However, most existing transformer-based trackers rely on increasingly large and deep networks to improve accuracy in complex backgrounds, which constrains their practicality in real-world deployments. To address this limitation, we propose a lightweight anti‑UAV tracker based on knowledge distillation. Specifically, we design a fully transformer-based anti‑UAV teacher network (TransADT) and develop a lightweight Siamese student network (SiamGLT) constructed with on a gated linear transformer, enabling accurate real-time tracking. In addition, we introduce a heterogeneous model knowledge distillation framework (HKD) to facilitate knowledge transfer from the large transformer teacher to the convolutional student network. This framework comprises three types of knowledge transfer: foreground and background feature decoupled distillation, instance-wise relation distillation, and response knowledge distillation. Comprehensive experiments on Anti-UAV benchmarks and real-world scenarios demonstrate that the proposed tracker surpasses other state-of-the-art methods. Our SiamGLT model achieves an optimal balance between tracking accuracy and computational efficiency.