Achieving long-term, continuous, and accurate localization for Unmanned Aerial Vehicles (UAVs) in outdoor GNSS-denied environments where pre-existing reference maps are available is challenging. To this end, this paper proposes a Cross-view Particle Filter Localization (CPFL) framework. Unlike existing particle filter approaches that rely on inertial sensors for state propagation or sparse semantic labels for observation updates, CPFL is a vision-driven solution. This framework introduces specific adaptations into the two core stages of particle filtering: In the motion propagation stage, it achieves visual state transition by calculating a feature-based inter-frame homography mapping to estimate the 2D global relative motion components, eliminating the dependency on inertial priors; in the observation correction stage, a Dual-Granularity Adaptive Gating (DGAG) cross-view network is designed to mitigate perceptual aliasing and generate discriminative absolute position weights for the particles. By fusing these two stages through a filter mechanism, the framework transforms unbounded cumulative drift into bounded absolute localization errors. Furthermore, addressing the measurement deficiencies of traditional single-frame metrics, this paper also proposes a Trajectory Continuity Index (TCI@d) tailored for continuous localization tasks. Experiments on the real-world MAFS dataset confirm that this framework achieves a mean localization error of 5.28 m and a localization success rate of 89.7% under a 10-m threshold. Compared with mainstream vision-only algorithms and IMU-fusion baselines, this framework demonstrates lower mean errors and improved trajectory continuity, validating its effectiveness for long-term robustness.

Unmanned aerial vehicle (UAV) self-localization in complex environments is critical when global navigation satellite systems (GNSSs) are unreliable. Existing datasets, often limited to low-altitude urban scenes, hinder generalization. This study introduces Multi-UAV, a novel dataset with 17.4 k high-resolution UAV–satellite image pairs from diverse terrains (urban, rural, mountainous, farmland, coastal) and altitudes across China, enhancing cross-view geolocalization research. We propose a lightweight value reduction pyramid transformer (VRPT) for efficient feature extraction and a residual feature pyramid network (RFPN) for multi-scale feature fusion. Using meter-level accuracy (MA@K) and relative distance score (RDS), VRPT achieves robust, high-precision localization across varied terrains, offering significant potential for resource-constrained UAV deployment.

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults.

Heterogeneous data from diverse sensor platforms can complement each other, providing more comprehensive and richer information for the analysis of complex scenarios. To effectively utilise this information for UAV geo-location tasks, accurate feature extraction and fusion methods are crucial. To address this challenge, we propose the Multi-Modal Geo Location Transformer Network (MMGLT), leveraging attention mechanisms to fuse cross-modal features. Unlike traditional dual modal fusion frameworks integrating only drone downward-view and satellite images, MMGLT innovatively uses a triple-input structure to process RGB images, LiDAR-derived auxiliary data, and satellite images. It also designs a tailored cross-attention mechanism to link visual semantics with remote sensing features f inely, enabling more robust multi-source feature learning. Subsequently, MMGLT employs a Global Attention Upsampling Head that refines features layer by layer via attention and adaptive up sampling; by combining dynamic feature re-allocation with multi scale pyramid fusion, it mitigates feature degradation and boosts representational power. With the aim of facilitating subsequent applications, we have successfully deployed the MMGLT model on the NVIDIA Jetson Orin NX compact computer. To evaluate the performance of MMGLT in the task, this paper constructs the CrossView-Van dataset. To the best of our knowledge, this is the first end-to-end geo-location dataset that integrates RGB images, high-precision remote sensing feature data, and satellite images. On this new dataset, MMGLT significantly outperforms existing single-modal methods, and achieves improvement in low-texture forest regions where traditional approaches underperform.