Search Results (205)

Unmanned aerial vehicles (UAVs) and deep learning (DL) have introduced a new framework for intelligent building façade defect detection, yet existing studies often focus on isolated technical components and lack a systematic evaluation of the entire pipeline. To address this gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles retrieved from the Web of Science database over the period 2021–2026. This review investigates four key domains: (1) UAV inspection path planning and data acquisition; (2) multi-modal data fusion; (3) DL-driven defect detection algorithms; and (4) 3D reconstruction and digital twin integration. Our analysis reveals the following main findings. Real-time perception-aware planning is central to UAV path planning, yet most studies lack robustness evaluations under real-world deployment conditions. Multi-modal data fusion improves detection across multiple defect types, yet edge deployment requires balancing lightweight design with recognition stability. Defect recognition algorithms increasingly adopt task-driven architectures, but limited edge-device resources demand joint optimization of efficiency and accuracy. In digital twins, systematic research is still lacking on semantically integrating recognition results into BIM for O&M decision-making, leaving the closed loop from defect detection to maintenance unresolved. This review aims to help researchers and practitioners advance UAV-based inspection from an auxiliary tool to a fully autonomous, reliable intelligent agent for refined management of the urban built environment. Full article

In the multi-class recognition task for unmanned aerial vehicle (UAV) radio frequency (RF) signals, noise interference and complex backgrounds can significantly degrade recognition performance. The stability and accuracy of existing recognition methods often fail to meet the requirements of practical applications. To enhance the stability and accuracy of UAV RF signal recognition, especially to mitigate performance degradation in complex backgrounds, a UAV RF signal classification method, MD-Net, is proposed that integrates Adaptive Time-Frequency Masking and a dual-network architecture. First, an Adaptive Time-Frequency Masking mechanism is constructed. By analyzing the energy distribution of RF signals in the time-frequency domain, the masking region is automatically determined, ensuring that the training data maintains a diverse distribution across different interference scenarios. This significantly improves the model’s anti-interference performance and discriminative stability in complex environments. Subsequently, a dual-branch recognition network architecture is designed, integrating a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The MLP extracts static amplitude features from the signals, while the LSTM learns time-series features. These two feature types are then fused to achieve complementary characteristics, ultimately enabling accurate classification of UAV RF signals. Extensive comparative experiments conducted on the DroneRF dataset demonstrate that the MD-Net model achieves an average recognition accuracy of 85.58%, an improvement of 5.27 percentage points over the baseline model. The experimental results show that Adaptive Time-Frequency Masking can effectively enhance the model’s adaptability to real-world interference environments, while the dual-network fusion mechanism fully integrates static amplitude and time-series features, providing a feasible and highly reliable technical approach for UAV RF signal recognition. Full article

Alzheimer’s disease (AD) lacks effective disease-modifying therapies, and extracellular vesicles (EVs) derived from adipose-derived mesenchymal stromal cells (ADMSCs) have emerged as promising therapeutic candidates. In this study, we investigated the brain biodistribution and dose-dependent effects of intranasally administered ADMSC-EVs in female APP/PS1 mice, with age-matched wild-type mice and vehicle-treated transgenic mice serving as controls. EV biodistribution was assessed using PKH26 labeling, cognitive performance was evaluated using the Morris water maze, Y-maze, and novel object recognition tests, and hippocampal amyloid pathology and plasma AD-related biomarkers were analyzed. Intranasally delivered ADMSC-EVs rapidly reached multiple brain regions, including the hippocampus, improved learning and memory performance, and reduced hippocampal amyloid-β 1-42 (Aβ42) deposition and plaque burden. These effects followed a nonlinear dose–response pattern, with reduced efficacy at low doses and no additional benefits at high doses. Notably, partial behavioral and pathological benefits persisted after treatment cessation. Together, these findings show that intranasal ADMSC-EVs exert therapeutic effects in APP/PS1 mice and support the importance of dose optimization and post-treatment durability in the development of EV-based interventions for AD. Full article

Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

To address the challenges of insufficient feature fusion and incomplete multi-scale information capture in complex traffic scenarios, we propose a vehicle type recognition network based on feature comparison and the Mixture of Experts (MoE) model. Specifically, the MobileNetV4 backbone is introduced to enhance deep feature extraction for vehicle targets. Meanwhile, we design a Multi-scale Interleaving Fusion Module (MSIFM), which progressively transmits feature channels via an interleaving structure to capture multi-scale features while enhancing vehicle feature representation. Moreover, we devise a Feature Compare Enhancement Module (FCEM) to efficiently fuse feature maps with different semantic information. By performing feature comparison, it strengthens strongly correlated features while suppressing weakly correlated ones. Finally, we design a Mixture of Experts Feature Enhancement Module (MOEFEM) to aggregate multi-scale feature maps and adaptively capture detailed vehicle features through multiple expert units. Experimental results demonstrate that our method achieves mAP improvements of 2.2% and 2.4% over YOLOv11 on UA-DETRAC and BDD100K, respectively. The proposed method not only improves detection accuracy significantly but also maintains real-time efficiency, providing a practical solution for high-precision vehicle type recognition. It offers valuable technical support for intelligent transportation systems, smart city management, and autonomous driving safety. Full article

Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient light, forcing drivers to rely on roadway lighting for hazard recognition. Improving illumination arrangements can significantly reduce the likelihood of crashes. However, there are significant differences in the effects of illumination on drivers’ visual search ability at different vehicle speeds. Therefore, the collaborative matching of illumination and speed limits can effectively improve traffic efficiency and reduce the probability of nighttime accidents. In this paper, we establish a collaborative optimization model of illumination and speed limits at rural highway intersections that considers drivers’ visual recognition of VRUs. We then design an experiment with illuminance, vehicle speed, and VRU type/location as control variables to collect recognition distances, and finally analyze their effects to calculate speed limits under different illuminances. Results indicate that pedestrians and cyclists appearing from the left side are recognized 24.73% and 15.79% earlier than those from the right, suggesting that VRUs from the right side are more vulnerable. Additionally, the safety benefit of improving illumination on increasing speed limits gradually diminishes as illuminance rises. Therefore, determining the most suitable illumination and speed limit configuration requires a comprehensive evaluation of the cost–benefit relationship between lighting investments and the gains resulting from higher speed limits. Full article

In the context of urban renewal, the spatial interface of historic commercial districts functions as both a carrier of historical character and a key setting for commercial activity, public life, and local cultural expression. To address the limitations of conventional studies that rely heavily on field observation and qualitative description, this study takes Xiaohe Zhijie in Hangzhou as a case and develops a multi-level street-view recognition framework for the quantitative analysis of spatial interface characteristics. Based on street-view image collection and standardized preprocessing, a sample database was established at the sampling-point scale. Semantic segmentation, automated commercial object detection, and manual interpretation were combined to identify interface elements, including buildings, sky, greenery, pavement, vehicles, pedestrians, and commercial objects, while commercial content was assessed in terms of locality and homogenization. The results show that Xiaohe Zhijie exhibits a building-dominated and relatively enclosed interface pattern, with greenery and pavement forming the basic environmental ground, weak vehicle interference, and localized enhancement of vitality through commercial objects and pedestrian activities. Significant differences were found among street segments in openness, commercial coverage, and local expression. Three interface types were identified: commercial–cultural composite, local life-oriented, and waterfront landscape–cultural composite. The main challenge lies not in commercialization itself, but in stronger visual locality than content locality and increasing homogenization, resulting in a pattern of “localized form but homogenized content.” Full article

Electrocardiogram (ECG)-based biometrics have emerged as a promising solution for continuous and intrinsic human identification; nevertheless, the robustness of these systems under realistic noise conditions remains a critical challenge for practical deployment. This work presents a theoretical and experimental analysis of how different noise types and levels affect ECG biometric recognition by comparing three methodological families: fiducial-based approaches using morphological features with traditional classifiers such as SVM and k-NN, non-fiducial methods based on signal compression and global descriptors, and Deep Learning models. Controlled distortions and additive noise injection into public ECG databases enable systematic quantification of feature degradation. Experimental validation is performed using the CardioWheel system, a real-world in-vehicle ECG acquisition platform, to evaluate performance under realistic motion and noise conditions. The methodological framework proposed for robustness evaluation and noise-aware training is inherently generic and can be extended to other biometric tasks subject to noise. Results show that different algorithmic families exhibit distinct resilience profiles under noise contamination and reveal a practical signal quality boundary for reliable ECG biometric recognition, with performance deteriorating under severe noise conditions. Noise-aware training improves robustness, particularly for Deep Learning and SVM-based classifiers, highlighting the trade-off between interpretability and robustness. By bridging theoretical analysis and applied experimentation, this work provides practical signal quality guidelines for real-world ECG biometric systems. Full article

As a next-generation serial communication protocol employed in automotive electronics and industrial control domains, Controller Area Network with Flexible Data-Rate (CAN-FD) enhances communication efficiency via the introduction of a dual-rate transmission mechanism, yet it still inherits the security vulnerabilities of traditional CAN networks. To enhance the security of node identity authentication in CAN-FD networks—a critical prerequisite for secure communication—we present an electronic control unit (ECU) authentication scheme that utilizes voltage hardware fingerprints (VHFs) as the core identity credential. Specifically, a single frame of data is utilized to integrate the control field’s voltage characteristics and data field’s edges, forming stable and distinguishable hardware fingerprints. We also analyze the VHF offset characteristics under typical spoofing attacks and wire-tapping attacks, and then propose a lightweight vehicle intrusion detection system (VIDS) scheme to identify attack scenarios and locate the compromised ECU in CAN-FD networks. Lastly, we conducted research on and discussed other VHF-influencing factors and put forward detailed specific solutions. Attack tests are conducted under four representative scenarios, namely substitution attack, masquerade attack, injection attack, and wire-tapping attack. The findings reveal that our scheme can not only accurately distinguish between various CAN-FD nodes but also identify specific attack types in real time. In detail, a single-frame node recognition rate exceeding 99% is achieved in approximately 2 ms, and in experiments covering multiple attack scenarios on this six-node prototype system, 100% recognition accuracy for attack types is realized in approximately 500 ms. Full article

Through this qualitative systematic review, the authors ask the following: To what extent is the 300-word abstract fit for purpose in representing art and design practice-based research outputs on small and/or specialist institutional repositories? The abstract is an important part of the metadata when an Arts Practice-Based Output (APBO) is deposited on a repository. APBOs are non-traditional item types resulting from creative/artistic research processes. Examples include exhibitions, artefacts and digital videos. Little is known about how effectively these abstracts communicate research processes and insights across the art and design sector. This study aims to investigate how well the abstract communicates information about the arts practice-based research through a systematic review of APBOs. The eligibility criteria for inclusion in the review were as follows: APBOs must be from the date range January 2019 to January 2024, be an item type where the 300-word abstract is required, the abstract must be part of the publicly available metadata for the item, and outputs must be practice-based and from the art and design field. The date range (2019–2024) was employed because, during this time, APBOs had gained recognition in the wider research environment. APBOs from the reviewers’ institutional repository were not included in this study to avoid bias that could skew the results of the review. The data repositories from small and/or specialist Higher Education Institutions in the United Kingdom were searched for outputs which appeared to meet the eligibility criteria. These types of institution prioritise and produce more of these output types. A quality tool appropriate for creative/artistic research was applied to the identified dataset of APBOs. The resulting 27 APBOs’ 300-word abstracts were analysed using a thematic approach. Findings suggest that the 300-word abstracts contained information about the quality indicators such as whether the project got funding, the identities of prestigious collaborators and/or dissemination vehicles, and the international recognition of the research. Other identified themes were methodologies, contribution to knowledge, subject matter and item type. Full article

Intelligent transportation systems (ITSs) are crucial for modern traffic management and law enforcement. This paper addresses the challenge of monitoring and managing extensive vehicle traffic in large cities like Lahore, Pakistan. We propose a deep learning based ITS utilizing Vision Transformers combined with convolutional feature extraction to accurately identify vehicle type, color, make/model, and license plates. Experiments were conducted on a comprehensive dataset collected from multiple checkpoints across Lahore under varying environmental conditions. Our proposed model achieved high accuracy rates: 98.0% for vehicle type classification, 96.0% for color detection, 95.0% for make/model identification, and 89.0% for license plate recognition. These results demonstrate the system’s potential to significantly enhance traffic management and road safety and support law enforcement operations in developing urban environments. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article

Reliable recognition of unmanned aerial vehicle (UAV) communication signals is essential for low-altitude airspace safety and UAV monitoring. In practical electromagnetic environments, UAV signals exhibit complex time-frequency characteristics, and unknown signal types frequently appear, making open-set recognition necessary. This paper proposes a geometry-energy open-set recognition (GE-OSR) method for UAV signal identification. First, a time-frequency convolutional hybrid network is developed to learn multi-scale representations from raw UAV signals. Then, learnable class embeddings with a dual-constraint embedding loss are introduced to improve feature compactness and separability. In addition, a free-energy alignment loss is introduced to assign low energy to known signals and high energy to unknown ones, forming an adaptive rejection boundary. Experiments under different signal-to-noise ratios (SNRs) and openness levels show that GE-OSR provides stable performance. At 0 dB SNR under high openness, the method improves OSCR by about 2.95% over the recent S3R model and more than 6% over other baselines. These results show that GE-OSR is effective for practical UAV signal identification and unknown signal detection in complex low-altitude environments. Full article