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This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation algorithm with hierarchical filtering is proposed, and this algorithm performs motion state estimation for optoelectronic loads and ground-motion targets separately. Using the laser range sensor measurements as constraints, the optoelectronic load angle state quantities are involved together in estimating the ground target motion state, resulting in improved accuracy of ground-motion target localization tracking and motion state estimation. The experimental data show that the UAV ground-motion target localization tracking and motion estimation algorithm using hierarchical filtering reduces the localization tracking error by at least 7.5 m and the motion state estimation error by at least 0.8 m/s compared to other algorithms.

In this work, we propose a geolocation framework for distant ground targets integrating laser rangefinder sensors with multimodal visual servo control. By simulating binocular visual servo measurements through monocular visual servo tracking at fixed time intervals, our approach requires only single-session sensor attitude correction calibration to accurately geolocalize multiple targets during a single flight, which significantly enhances operational efficiency in multi-target geolocation scenarios. We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. The experimental results show that through the UAV’s continuous reconnaissance measurements, the geolocalization error of remote ground targets based on our algorithm is less than 7 m for 3000 m, and less than 3.5 m for 1500 m. We have realized the fast and high-precision geolocalization of remote targets on the ground under the high-altitude reconnaissance of UAVs.

To address the larger numbers of small objects and the issues of occlusion and clustering in UAV aerial photography, which can lead to false positives and missed detections, we propose an improved small object detection algorithm for UAV aerial scenarios called YOLOv8 with tiny prediction head and Space-to-Depth Convolution (HSP-YOLOv8). Firstly, a tiny prediction head specifically for small targets is added to provide higher-resolution feature mapping, enabling better predictions. Secondly, we designed the Space-to-Depth Convolution (SPD-Conv) module to mitigate the loss of small target feature information and enhance the robustness of feature information. Lastly, soft non-maximum suppression (Soft-NMS) is used in the post-processing stage to improve accuracy by significantly reducing false positives in the detection results. In experiments on the Visdrone2019 dataset, the improved algorithm increased the detection precision mAP0.5 and mAP0.5:0.95 values by 11% and 9.8%, respectively, compared to the baseline model YOLOv8s.

Single-stage object detection networks are widely applied in various scenarios due to their high precision and speed. These networks typically consist of three parts structurally: backbone, neck, and head. Among these, multi-scale feature fusion in the neck is a crucial step to enhance detection effectiveness in object detection. Multi-scale feature fusion typically involves combining features from different layers through addition or concatenation. However, such methods often focus solely on the shared functionality of neighboring scale features, neglecting detailed information from higher-level features, leading to significant information loss. To address these issues, this paper proposes a novel network called GMDNet, comprising two key modules: a Global Information Sharing Module (GISM) and a Detailed Information Extraction Module (DIEM). GISM addresses different scale features with high and low-level branches, aligning and merging information across layers using structural reparameterization and Transformer techniques to enhance global information flow among different hierarchical features. DIEM extracts detailed information using three-feature fusion mapping and further integrates multi-scale information with detailed features using channel and positional attention mechanisms, effectively reintegrating previously overlooked details. Experiments conducted on multiple public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods and achieves an ideal balance between speed and accuracy across all model scales.

This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from semantic maps to provide initial information on task points for UAV formations, thereby planning formation paths that meet practical requirements. Firstly, a semantic segmentation network model based on multi-scale feature extraction and fusion is employed to obtain UAV aerial semantic maps containing environmental information. Secondly, based on the semantic maps, a three-point optimization model for the optimal UAV trajectory is established, and a general formula for calculating the heading angle is proposed to approximately decouple the triangular equation of the optimal trajectory. For large-scale formations and task points, an improved fuzzy clustering algorithm is proposed to classify task points that meet distance constraints by clusters, thereby reducing the computational scale of single samples without changing the sample size and improving the allocation efficiency of the UAV formation path planning model. Experimental data show that the UAV cluster path planning method using angle-optimized fuzzy clustering achieves an 8.6% improvement in total flight range compared to other algorithms and a 17.4% reduction in the number of large-angle turns.

Cooperative path planning for multiple Unmanned Aerial Vehicles (UAVs) within complex mountainous terrain presents a unique challenge, characterized by a high-dimensional search space fraught with numerous local optima. Conventional metaheuristic algorithms often fail in such deceptive landscapes due to premature convergence stemming from a static balance between exploration and exploitation. To overcome the aforementioned limitations, this paper develops the Reinforcement Learning-guided Hybrid Sparrow Search Algorithm (RLHSSA), an optimization framework specifically engineered for robust navigation in complex topographies. The core innovation of RLHSSA lies in its two-level architecture. At a lower level, a purpose-built operator suite provides specialized tools essential for mountain environments: robust exploration strategies, including Levy Flight, to escape the abundant local optima, and an Elite-SSA for the high-precision exploitation needed to refine paths within narrow corridors. At a higher level, a reinforcement learning agent intelligently selects the most suitable operator to adapt the search strategy to the terrain’s complexity in real-time. This adaptive scheduling mechanism is the key to achieving a superior exploration–exploitation balance, enabling the algorithm to effectively navigate the intricate problem landscape. Extensive simulations within challenging mountainous environments demonstrate that RLHSSA consistently outperforms state-of-the-art algorithms in solution quality and stability, validating its practical potential for high-stakes multi-UAV mission planning.

Dry eye is among the most prevalent complications following refractive surgery, significantly impacting the training and daily lives of recruits. While recent years we have witnessed some advancements in understanding the occurrence and progression of dry eye, the specific effects of refractive surgery on this condition remain unclear. This study aims to investigate the impact of refractive surgery on dry eye among 300 army recruits. A series of examinations specific to dry eye were conducted on the subjects using the OSDI questionnaire and the ocular surface comprehensive analyzer. The correlation between refractive surgery and dry eye was analyzed in conjunction with dry eye symptoms and related data. Additionally, optical coherence tomography (OCT) was employed to observe fundus changes in dry eye patients, comparing those with low to moderate myopia against patients with high myopia prior to surgery. The morphology of the meibomian glands in the upper eyelid was assessed using the Keratograph 5M ocular surface analyzer, where the area of meibomian gland loss was calculated and scored, facilitating an exploration of the relationship between meibomian gland loss and dry eye. Furthermore, the dry eye detection rates of Non-Invasive Break-Up Time (NIBUT), Lipid Layer Thickness Measurement (LTMH), and basal Schirmer secretion (SIT) were calculated, and the diagnostic differences among these three methods for dry eye were analyzed. According to the OSDI questionnaire, 117 (39%) patients opted for SMILE, 60 (20%) for F-LASIK, and 123 (41%) for LASIK. Among the recruits, 78 (26%) were diagnosed with dry eye following refractive surgery. Single factor and multiple logistic regression analyses indicated that LASIK may serve as an independent risk factor for the development of dry eye after refractive surgery. Furthermore, the incidence of leopard-pattern fundus was significantly higher in recruits with high myopia compared to those with low to moderate myopia. Keratograph5M assessments revealed that 52.6% of patients exhibited no meibomian gland loss; 45.5% had meibomian gland loss of less than 1/3; 1.9% experienced meibomian gland loss ranging from 1/3 to 2/3; and no patients had meibomian gland loss exceeding 2/3. These results suggest that there is no direct relationship between dry eye and meibomian gland loss following refractive surgery. Additionally, the dry eye detection rate using non-invasive tear break-up time (NIBUT) was 92.6%, while the detection rate for LTMH dry eye was 91.0%. The basal Schirmer test (SIT) yielded a dry eye detection rate of 78.2%, indicating that non-invasive tear assessment methods have a higher detection rate for dry eye. The incidence of dry eye among recruits following excimer laser corneal refractive surgery is significantly higher than that observed with other surgical methods, suggesting that it may represent an independent risk factor for dry eye post-refractive surgery. Consequently, we do not recommend the use of laser in situ keratomileusis for refractive surgery.

This study primarily studies the shortest-path planning problem for unmanned aerial vehicle (UAV) formations under uncertain target sequences. In order to enhance the efficiency of collaborative search in drone clusters, a compensation look-ahead algorithm based on optimizing the four-point heading angles is proposed. Building upon the receding-horizon algorithm, this method introduces the heading angles of adjacent points to approximately compensate and decouple the triangular equations of the optimal trajectory, and a general formula for calculating the heading angles is proposed. The simulation data indicate that the model using the compensatory look forward algorithm exhibits a maximum improvement of 12.9% compared to other algorithms. Furthermore, to solve the computational complexity and sample size requirements for optimal solutions in the Dubins multiple traveling salesman model, a path-planning model for multiple UAV formations is introduced based on the Euclidean traveling salesman problem (ETSP) pre-allocation. By pre-allocating sub-goals, the model reduces the computational scale of individual samples while maintaining a constant sample size. The simulation results show an 8.4% and 17.5% improvement in sparse regions for the proposed Euclidean Dubins traveling salesman problem (EDTSP) model for takeoff from different points.

Using images captured by drone cameras and comparing them with known Google satellite maps to obtain the current location of the drone is an important way of UAV navigation in GPS-denied environments. But, due to inherent modality differences and significant geometric deformations, cross-modal image registration is challenging. This paper proposes a CNN-Transformer hybrid network model for feature detection and feature matching. ResNet50 is used as the backbone network for feature extraction. An improved feature fusion module is used to fuse feature maps from different levels, and then a Transformer encoder–decoder structure is used for feature matching to obtain preliminary correspondences. Finally, a geometric outlier removal method (GSM) is used to eliminate mismatched points based on the geometric similarity of inliers, resulting in more robust correspondences. Qualitative and quantitative experiments were conducted on multimodal image datasets captured by UAVs; the correct matching rate was improved by 52%, 21%, and 15%, respectively, and the error was reduced by 36% compared to the 3MRS algorithm. A total of 56 experiments were conducted in actual scenarios, with a localization success rate of 91.1%, and the RMSE of UAV positioning was 4.6 m.

In general, path planning for unmanned aerial vehicles (UAVs) is modeled as a challenging optimization problem that is critical to ensuring efficient UAV mission execution. The challenge lies in the complexity and uncertainty of flight scenarios, particularly in three-dimensional scenarios. In this study, one introduces a framework for UAV path planning in a 3D environment. To tackle this challenge, we develop an innovative hybrid gray wolf optimizer (GWO) algorithm, named SDPGWO. The proposed algorithm simplifies the position update mechanism of GWO and incorporates a differential perturbation strategy into the search process, enhancing the optimization ability and avoiding local minima. Simulations conducted in various scenarios reveal that the SDPGWO algorithm excels in rapidly generating superior-quality paths for UAVs. In addition, it demonstrates enhanced robustness in handling complex 3D environments and outperforms other related algorithms in both performance and reliability.