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Search Results (2,090)

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21 pages, 418 KiB  
Article
Resistance of an Emerging Community: Early Christians Facing Adversity
by Miguel-Ángel García-Madurga
Histories 2025, 5(3), 38; https://doi.org/10.3390/histories5030038 (registering DOI) - 16 Aug 2025
Abstract
Situated at the intersection of social history and psychology, this study examines how early Christian communities in Bithynia-Pontus navigated the persecution narrated in Pliny the Younger’s Epistle X 96. Through systematic textual analysis of Latin and Greek sources—triangulated with comparative evidence from Tacitus [...] Read more.
Situated at the intersection of social history and psychology, this study examines how early Christian communities in Bithynia-Pontus navigated the persecution narrated in Pliny the Younger’s Epistle X 96. Through systematic textual analysis of Latin and Greek sources—triangulated with comparative evidence from Tacitus and corroborating archaeological data—and interpreted through Conservation-of-Resources and Social Identity theoretical frameworks, we reconstruct the repertoire of collective coping strategies mobilised under Roman repression. Our findings show that ritualised dawn assemblies, mutual economic assistance, and a theologically grounded expectation of post-mortem vindication converted external coercion into internal cohesion; these practices neutralised informer threat, sustained group morale, and ultimately expanded Christian networks across Asia Minor. Moreover, Pliny’s ad hoc judicial improvisations reveal the governor’s own bounded rationality, underscoring the reciprocal nature of stress between the persecutor and persecuted. By mapping the dynamic interaction between imperial policy and subaltern agency, the article clarifies why limited, locally triggered violence consolidated rather than extinguished the nascent movement. The analysis contributes a theoretically informed, evidence-based account of religious-minority resilience, enriching both early Christian historiography and broader debates on group survival under systemic duress. Full article
(This article belongs to the Section Political, Institutional, and Economy History)
33 pages, 10397 KiB  
Article
Multi-AUV Dynamic Cooperative Path Planning with Hybrid Particle Swarm and Dynamic Window Algorithm in Three-Dimensional Terrain and Ocean Current Environment
by Bing Sun and Ziang Lv
Biomimetics 2025, 10(8), 536; https://doi.org/10.3390/biomimetics10080536 - 15 Aug 2025
Abstract
Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization [...] Read more.
Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO–DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 3rd Edition)
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21 pages, 9031 KiB  
Article
A Pyramid Convolution-Based Scene Coordinate Regression Network for AR-GIS
by Haobo Xu, Chao Zhu, Yilong Wang, Huachen Zhu and Wei Ma
ISPRS Int. J. Geo-Inf. 2025, 14(8), 311; https://doi.org/10.3390/ijgi14080311 - 15 Aug 2025
Abstract
Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes [...] Read more.
Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes in scale. This oversight results in less stable localization performance and challenges in coping with dynamic environments. To address these tasks, we propose a pyramid convolution-based scene coordinate regression network (PSN). Our approach leverages a pyramidal convolutional structure, integrating kernels of varying sizes and depths, alongside grouped convolutions that alleviate computational demands while capturing multi-scale features from the input imagery. Subsequently, the network incorporates a novel randomization strategy, effectively diminishing correlated gradients and markedly bolstering the training process’s efficiency. The culmination lies in a regression layer that maps the 2D pixel coordinates to their corresponding 3D scene coordinates with precision. The experimental outcomes show that our proposed method achieves centimeter-level accuracy in small-scale scenes and decimeter-level accuracy in large-scale scenes after only a few minutes of training. It offers a favorable balance between localization accuracy and efficiency, and effectively supports augmented reality visualization in dynamic environments. Full article
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30 pages, 16545 KiB  
Article
The Socius in Architectural Pedagogy: Transformative Design Studio Teaching Models
by Ashraf M. Salama and Madhavi P. Patil
Architecture 2025, 5(3), 61; https://doi.org/10.3390/architecture5030061 - 15 Aug 2025
Abstract
Despite a global trend toward socially engaged higher education, architectural pedagogy continues to grapple for a coherent approach that systematically and genuinely integrates socio-cultural dimensions into design studio teaching practices. Defined as the interwoven social, cultural, and political factors that shape the built [...] Read more.
Despite a global trend toward socially engaged higher education, architectural pedagogy continues to grapple for a coherent approach that systematically and genuinely integrates socio-cultural dimensions into design studio teaching practices. Defined as the interwoven social, cultural, and political factors that shape the built environment, the socius is treated peripherally within architectural pedagogy, limiting students’ capacity to develop civic agency, spatial justice awareness, and critical reflexivity in navigating complex societal conditions. This article argues for a socius-centric reorientation of architectural pedagogy, postulating that socially engaged studio models, which include Community Design, Design–Build, and Live Project, must be conceptually integrated to fully harness their pedagogical merits. The article adopts two lines of inquiry: first, mapping the theoretical underpinnings of the socius across award-winning pedagogical innovations and Google Scholar citation patterns; and second, defining the core attributes of socially engaged pedagogical models through a bibliometric analysis of 87 seminal publications. Synthesising the outcomes of these inquiries, the study offers an advanced articulation of studio learning as a process of social construction, where architectural knowledge is co-produced through role exchange, iterative feedback, interdisciplinary dialogue, and emergent agency. Conclusions are drawn to offer pragmatic and theoretically grounded pathways to reshape studio learning as a site of civic transformation. Full article
(This article belongs to the Special Issue Spaces and Practices of Everyday Community Resilience)
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22 pages, 4524 KiB  
Article
RAEM-SLAM: A Robust Adaptive End-to-End Monocular SLAM Framework for AUVs in Underwater Environments
by Yekai Wu, Yongjie Li, Wenda Luo and Xin Ding
Drones 2025, 9(8), 579; https://doi.org/10.3390/drones9080579 - 15 Aug 2025
Abstract
Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) [...] Read more.
Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) provides a cost-effective alternative for AUV navigation, existing methods are primarily designed for terrestrial applications and struggle to address underwater-specific issues, such as poor illumination, dynamic interference, and sparse features. To tackle these challenges, we propose RAEM-SLAM, a robust adaptive end-to-end monocular SLAM framework for AUVs in underwater environments. Specifically, we propose a Physics-guided Underwater Adaptive Augmentation (PUAA) method that dynamically converts terrestrial scene datasets into physically realistic pseudo-underwater images for the augmentation training of RAEM-SLAM, improving the system’s generalization and adaptability in complex underwater scenes. We also introduce a Residual Semantic–Spatial Attention Module (RSSA), which utilizes a dual-branch attention mechanism to effectively fuse semantic and spatial information. This design enables adaptive enhancement of key feature regions and suppression of noise interference, resulting in more discriminative feature representations. Furthermore, we incorporate a Local–Global Perception Block (LGP), which integrates multi-scale local details with global contextual dependencies to significantly improve AUV pose estimation accuracy in dynamic underwater scenes. Experimental results on real-world underwater datasets demonstrate that RAEM-SLAM outperforms state-of-the-art SLAM approaches in enabling precise and robust navigation for AUVs. Full article
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24 pages, 2716 KiB  
Article
Interactive Indoor Audio-Map as a Digital Equivalent of the Tactile Map
by Dariusz Gotlib, Krzysztof Lipka and Hubert Świech
Appl. Sci. 2025, 15(16), 8975; https://doi.org/10.3390/app15168975 - 14 Aug 2025
Abstract
There are still relatively few applications that serve the function of a traditional tactile map, allowing visually impaired individuals to explore a digital map by sliding their fingers across it. Moreover, existing technological solutions either lack a spatial learning mode or provide only [...] Read more.
There are still relatively few applications that serve the function of a traditional tactile map, allowing visually impaired individuals to explore a digital map by sliding their fingers across it. Moreover, existing technological solutions either lack a spatial learning mode or provide only limited functionality, focusing primarily on navigating to a selected destination. To address these gaps, the authors have proposed an original concept for an indoor mobile application that enables map exploration by sliding a finger across the smartphone screen, using audio spatial descriptions as the primary medium for conveying information. The spatial descriptions are hierarchical and contextual, focusing on anchoring them in space and indicating their extent of influence. The basis for data management and analysis is GIS technology. The application is designed to support spatial orientation during user interaction with the digital map. The research emphasis was on creating an effective cartographic communication message, utilizing voice-based delivery of spatial information stored in a virtual building model (within a database) and tags placed in real-world buildings. Techniques such as Text-to-Speech, TalkBack, QRCode technologies were employed to achieve this. Preliminary tests conducted with both blind and sighted people demonstrated the usefulness of the proposed concept. The proposed solution supporting people with disabilities can also be useful and attractive to all users of navigation applications and may affect the development of such applications. Full article
(This article belongs to the Section Earth Sciences)
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17 pages, 7735 KiB  
Article
A Recursive Truncated Taylor Expansion Downward Continuation Method for Geomagnetic Field
by Ke Wan, Haibin Li, Xu Liu, Zhongyan Liu, Yujing Xu, Yujie Xiang, Zengquan Ding, Weiji Dai, Xinrong He and Qi Zhang
Appl. Sci. 2025, 15(16), 8913; https://doi.org/10.3390/app15168913 - 13 Aug 2025
Viewed by 96
Abstract
In aeromagnetic detection and geomagnetic navigation, the reference geomagnetic maps usually need to be continued to different altitudes. Traditionally, the geomagnetic field upward continuation is stable. Nevertheless, the downward continuation is instable near the magnetic source and sensitive to the high-frequency noise. To [...] Read more.
In aeromagnetic detection and geomagnetic navigation, the reference geomagnetic maps usually need to be continued to different altitudes. Traditionally, the geomagnetic field upward continuation is stable. Nevertheless, the downward continuation is instable near the magnetic source and sensitive to the high-frequency noise. To address the problem, this article proposes a recursive truncated Taylor expansion (RTTE) downward continuation method for geomagnetic field. This method models the geomagnetic field in the vertical direction. The coefficients of the model are calculated based on the harmonicity of the geomagnetic field to ensure stability; a recursive process is implemented to extend its effect under a large continuation distance. To demonstrate the effectiveness of the proposed method, this paper compares the effects of the traditional Landweber iteration method and the proposed method using simulation data and real measured data. Under real measurement conditions, the MAE and RMSE of the proposed RTTE method are 0.1878 nT and 0.3184 nT, respectively, representing a reduction of 90.33% and 95.75% compared to the Landweber iteration method. The results show that the proposed RTTE method significantly improves the continuation accuracy compared with traditional methods, providing support for geomagnetic navigation and aeromagnetic exploration. Full article
(This article belongs to the Section Applied Physics General)
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34 pages, 9218 KiB  
Article
SC-YOLO: A Real-Time CSP-Based YOLOv11n Variant Optimized with Sophia for Accurate PPE Detection on Construction Sites
by Teerapun Saeheaw
Buildings 2025, 15(16), 2854; https://doi.org/10.3390/buildings15162854 - 12 Aug 2025
Viewed by 274
Abstract
Despite advances in YOLO-based PPE detection, existing approaches primarily focus on architectural modifications. However, these approaches overlook second-order optimization methods for navigating complex loss landscapes in object detection. This study introduces SC-YOLO, integrating CSPDarknet backbone with Sophia optimization (leveraging efficient Hessian estimates for [...] Read more.
Despite advances in YOLO-based PPE detection, existing approaches primarily focus on architectural modifications. However, these approaches overlook second-order optimization methods for navigating complex loss landscapes in object detection. This study introduces SC-YOLO, integrating CSPDarknet backbone with Sophia optimization (leveraging efficient Hessian estimates for curvature-aware updates) for enhanced PPE detection on construction sites. The proposed methodology includes three key steps: (1) systematic evaluation of EfficientNet, DINOv2, and CSPDarknet backbones, (2) integration of Sophia second-order optimizer with CSPDarknet for curvature-aware updates, and (3) cross-dataset validation in diverse construction scenarios. Traditional manual PPE inspection exhibits operational limitations, including high error rates (12–15%) and labor-intensive processes. SC-YOLO addresses these challenges through automated detection with potential for real-time deployment in construction safety applications. Experiments on VOC2007-1 and ML-31005 datasets demonstrate improved performance, achieving 96.3–97.6% mAP@0.5 and 63.6–68.6% mAP@0.5:0.95. Notable gains include a 9.03% improvement in detecting transparent objects. The second-order optimization achieves faster convergence with 7% computational overhead compared to baseline methods, showing enhanced robustness over conventional YOLO variants in complex construction environments. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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22 pages, 3506 KiB  
Article
UAV Navigation Using EKF-MonoSLAM Aided by Range-to-Base Measurements
by Rodrigo Munguia, Juan-Carlos Trujillo and Antoni Grau
Drones 2025, 9(8), 570; https://doi.org/10.3390/drones9080570 - 12 Aug 2025
Viewed by 86
Abstract
This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial [...] Read more.
This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article
(This article belongs to the Section Drone Design and Development)
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26 pages, 10272 KiB  
Article
Research on Disaster Environment Map Fusion Construction and Reinforcement Learning Navigation Technology Based on Air–Ground Collaborative Multi-Heterogeneous Robot Systems
by Hongtao Tao, Wen Zhao, Li Zhao and Junlong Wang
Sensors 2025, 25(16), 4988; https://doi.org/10.3390/s25164988 - 12 Aug 2025
Viewed by 329
Abstract
The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to [...] Read more.
The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to achieve rapid three-dimensional space coverage and complex terrain crossing for rapid and efficient map construction. Meanwhile, it utilizes the stable operation capability of an unmanned ground vehicle (UGV) and the ground detail survey capability to achieve precise map construction. The maps constructed by the two are accurately integrated to obtain precise disaster environment maps. Among them, the map construction and positioning technology is based on the FAST LiDAR–inertial odometry 2 (FAST-LIO2) framework, enabling the robot to achieve precise positioning even in complex environments, thereby obtaining more accurate point cloud maps. Before conducting map fusion, the point cloud is preprocessed first to reduce the density of the point cloud and also minimize the interference of noise and outliers. Subsequently, the coarse and fine registrations of the point clouds are carried out in sequence. The coarse registration is used to reduce the initial pose difference of the two point clouds, which is conducive to the subsequent rapid and efficient fine registration. The coarse registration uses the improved sample consensus initial alignment (SAC-IA) algorithm, which significantly reduces the registration time compared with the traditional SAC-IA algorithm. The precise registration uses the voxelized generalized iterative closest point (VGICP) algorithm. It has a faster registration speed compared with the generalized iterative closest point (GICP) algorithm while ensuring accuracy. In reinforcement learning navigation, we adopted the deep deterministic policy gradient (DDPG) path planning algorithm. Compared with the deep Q-network (DQN) algorithm and the A* algorithm, the DDPG algorithm is more conducive to the robot choosing a better route in a complex and unknown environment, and at the same time, the motion trajectory is smoother. This paper adopts Gazebo simulation. Compared with physical robot operation, it provides a safe, controllable, and cost-effective environment, supports efficient large-scale experiments and algorithm debugging, and also supports flexible sensor simulation and automated verification, thereby optimizing the overall testing process. Full article
(This article belongs to the Section Navigation and Positioning)
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27 pages, 15885 KiB  
Article
Model-Free UAV Navigation in Unknown Complex Environments Using Vision-Based Reinforcement Learning
by Hao Wu, Wei Wang, Tong Wang and Satoshi Suzuki
Drones 2025, 9(8), 566; https://doi.org/10.3390/drones9080566 - 12 Aug 2025
Viewed by 354
Abstract
Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency [...] Read more.
Autonomous UAV navigation in unknown and complex environments remains a core challenge, especially under limited sensing and computing resources. While most methods rely on modular pipelines involving mapping, planning, and control, they often suffer from poor real-time performance, limited adaptability, and high dependency on accurate environment models. Moreover, many deep-learning-based solutions either use RGB images prone to visual noise or optimize only a single objective. In contrast, this paper proposes a unified, model-free vision-based DRL framework that directly maps onboard depth images and UAV state information to continuous navigation commands through a single convolutional policy network. This end-to-end architecture eliminates the need for explicit mapping and modular coordination, significantly improving responsiveness and robustness. A novel multi-objective reward function is designed to jointly optimize path efficiency, safety, and energy consumption, enabling adaptive flight behavior in unknown complex environments. The trained policy demonstrates generalization in diverse simulated scenarios and transfers effectively to real-world UAV flights. Experiments show that our approach achieves stable navigation and low latency. Full article
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36 pages, 13404 KiB  
Article
A Multi-Task Deep Learning Framework for Road Quality Analysis with Scene Mapping via Sim-to-Real Adaptation
by Rahul Soans, Ryuichi Masuda and Yohei Fukumizu
Appl. Sci. 2025, 15(16), 8849; https://doi.org/10.3390/app15168849 - 11 Aug 2025
Viewed by 219
Abstract
Robust perception of road surface conditions is a critical challenge for the safe deployment of autonomous vehicles and the efficient management of transportation infrastructure. This paper introduces a synthetic data-driven deep learning framework designed to address this challenge. We present a large-scale, procedurally [...] Read more.
Robust perception of road surface conditions is a critical challenge for the safe deployment of autonomous vehicles and the efficient management of transportation infrastructure. This paper introduces a synthetic data-driven deep learning framework designed to address this challenge. We present a large-scale, procedurally generated 3D synthetic dataset created in Blender, featuring a diverse range of road defects—including cracks, potholes, and puddles—alongside crucial road features like manhole covers and patches. Crucially, our dataset provides dense, pixel-perfect annotations for segmentation masks, depth maps, and camera parameters (intrinsic and extrinsic). Our proposed model leverages these rich annotations in a multi-task learning framework that jointly performs road defect segmentation and depth estimation, enabling a comprehensive geometric and semantic understanding of the road environment. A core contribution is a two-stage domain adaptation strategy to bridge the synthetic-to-real gap. First, we employ a modified CycleGAN with a segmentation-aware loss to translate synthetic images into a realistic domain while preserving defect fidelity. Second, during model training, we utilize a dual-discriminator adversarial approach, applying alignment at both the feature and output levels to minimize domain shift. Benchmarking experiments validate our approach, demonstrating high accuracy and computational efficiency. Our model excels in detecting subtle or occluded defects, attributed to an occlusion-aware loss formulation. The proposed system shows significant promise for real-time deployment in autonomous navigation, automated infrastructure assessment and Advanced Driver-Assistance Systems (ADAS). Full article
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30 pages, 7398 KiB  
Article
A Study on UAV Path Planning for Navigation Mark Inspection Using Two Improved SOM Algorithms
by Liangkun Xu, Zaiwei Zhu, Zhihui Hu, Liyan Cai, Xinqiang Chen and Xiaomeng Wang
J. Mar. Sci. Eng. 2025, 13(8), 1537; https://doi.org/10.3390/jmse13081537 - 10 Aug 2025
Viewed by 242
Abstract
With the widespread application of unmanned aerial vehicle technology in navigation mark inspection, path planning algorithm efficiency has become crucial to improve inspection effectiveness. The traditional self-organizing mapping (SOM) algorithm suffers from dual limitations in UAV inspection path optimization, including insufficient global exploration [...] Read more.
With the widespread application of unmanned aerial vehicle technology in navigation mark inspection, path planning algorithm efficiency has become crucial to improve inspection effectiveness. The traditional self-organizing mapping (SOM) algorithm suffers from dual limitations in UAV inspection path optimization, including insufficient global exploration during early training stages and susceptibility to local optima entrapment in later stages, resulting in limited inspection efficiency and increased operational costs. For this reason, this study proposes two improved self-organizing mapping algorithms. First, the ORC_SOM algorithm incorporating a generalized competition mechanism and local infiltration strategy is developed. Second, the ORCTS_SOM hybrid optimization model is constructed by integrating the Tabu Search algorithm. Validation using two different scale navigation mark datasets shows that compared with traditional methods, the proposed improved methods achieve significantly enhanced path planning optimization. This study provides effective path planning methods for unmanned aerial vehicle navigation mark inspection, offering algorithmic support for intelligent maritime supervision system construction. Full article
(This article belongs to the Section Ocean Engineering)
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31 pages, 47425 KiB  
Article
T360Fusion: Temporal 360 Multimodal Fusion for 3D Object Detection via Transformers
by Khanh Bao Tran, Alexander Carballo and Kazuya Takeda
Sensors 2025, 25(16), 4902; https://doi.org/10.3390/s25164902 - 8 Aug 2025
Viewed by 215
Abstract
Object detection plays a significant role in various industrial and scientific domains, particularly in autonomous driving. It enables vehicles to detect surrounding objects, construct spatial maps, and facilitate safe navigation. To accomplish these tasks, a variety of sensors have been employed, including LiDAR, [...] Read more.
Object detection plays a significant role in various industrial and scientific domains, particularly in autonomous driving. It enables vehicles to detect surrounding objects, construct spatial maps, and facilitate safe navigation. To accomplish these tasks, a variety of sensors have been employed, including LiDAR, radar, RGB cameras, and ultrasonic sensors. Among these, LiDAR and RGB cameras are frequently utilized due to their advantages. RGB cameras offer high-resolution images with rich color and texture information but tend to underperform in low light or adverse weather conditions. In contrast, LiDAR provides precise 3D geometric data irrespective of lighting conditions, although it lacks the high spatial resolution of cameras. Recently, thermal cameras have gained significant attention in both standalone applications and in combination with RGB cameras. They offer strong perception capabilities under low-visibility conditions or adverse weather conditions. Multimodal sensor fusion effectively overcomes individual sensor limitations. In this paper, we propose a novel multimodal fusion method that integrates LiDAR, a 360 RGB camera, and a 360 thermal camera to fully leverage the strengths of each modality. Our method employs a feature-level fusion strategy that temporally accumulates and synchronizes multiple LiDAR frames. This design not only improves the detection accuracy but also enhances the spatial coverage and robustness. The use of 360 images significantly reduces blind spots and provides comprehensive environmental awareness, which is especially beneficial in complex or dynamic scenes. Full article
(This article belongs to the Section Sensing and Imaging)
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30 pages, 10586 KiB  
Article
Autonomous UAV-Based System for Scalable Tactile Paving Inspection
by Tong Wang, Hao Wu, Abner Asignacion, Zhengran Zhou, Wei Wang and Satoshi Suzuki
Drones 2025, 9(8), 554; https://doi.org/10.3390/drones9080554 - 7 Aug 2025
Viewed by 316
Abstract
Tactile pavings (Tenji Blocks) are prone to wear, obstruction, and improper installation, posing significant safety risks for visually impaired pedestrians. This system incorporates a lightweight YOLOv8 (You Only Look Once version 8) model for real-time detection using a fisheye camera to maximize field-of-view [...] Read more.
Tactile pavings (Tenji Blocks) are prone to wear, obstruction, and improper installation, posing significant safety risks for visually impaired pedestrians. This system incorporates a lightweight YOLOv8 (You Only Look Once version 8) model for real-time detection using a fisheye camera to maximize field-of-view coverage, which is highly advantageous for low-altitude UAV navigation in complex urban settings. To enable lightweight deployment, a novel Lightweight Shared Detail Enhanced Oriented Bounding Box (LSDE-OBB) head module is proposed. The design rationale of LSDE-OBB leverages the consistent structural patterns of tactile pavements, enabling parameter sharing within the detection head as an effective optimization strategy without significant accuracy compromise. The feature extraction module is further optimized using StarBlock to reduce computational complexity and model size. Integrated Contextual Anchor Attention (CAA) captures long-range spatial dependencies and refines critical feature representations, achieving an optimal speed–precision balance. The framework demonstrates a 25.13% parameter reduction (2.308 M vs. 3.083 M), 46.29% lower GFLOPs, and achieves 11.97% mAP50:95 on tactile paving datasets, enabling real-time edge deployment. Validated through public/custom datasets and actual UAV flights, the system realizes robust tactile paving detection and stable navigation in complex urban environments via hierarchical control algorithms for dynamic trajectory planning and obstacle avoidance, providing an efficient and scalable platform for automated infrastructure inspection. Full article
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