Search Results (167)

The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies. Full article

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient. Full article

In the context of rapidly growing logistics demand, traditional warehouse management methods are inadequate in meeting contemporary efficiency and accuracy requirements. The present study proposes the development of an intelligent warehouse visualization platform, the objective of which is to address issues such as high labor dependency, opaque inventory, and operational inefficiencies. The construction of a virtual warehouse environment was undertaken using Unity3D, with the aim of simulating real-world zones. These comprised storage areas, automatic guided vehicle (AGV) pathways, and operational spaces. The platform incorporates radio frequency identification devices (RFID) for item tracking and a role-based access system, enabling real-time monitoring and management of inbound, inventory, and outbound processes. In order to optimize AGV path planning, the proposed algorithm incorporates a dynamic weighted heuristic, a five-neighborhood search, a bidirectional search, and Bézier curve-based smoothing. The efficacy of these enhancements has been demonstrated through a reduction in searched nodes, computation time, and path length, while simultaneously enhancing smoothness. As demonstrated by simulations conducted in Unity3D, the optimized algorithm exhibits a reduction in search nodes of 59.19%, in time of 45.41%, and in path length of 18%, in comparison with the conventional A-star algorithm. The platform offers a safe, efficient, and scalable solution for enterprise training and operational simulation, contributing valuable insights for intelligent warehouse upgrading. Full article

The rapid growth of electric vehicle (EV) charging demand poses significant challenges to distribution networks (DNs), particularly during public holidays when concentrated peaks occur near scenic areas and urban transport hubs. These sudden surges can strain transformer capacity and compromise supply reliability. Fixed soft open points (SOPs) are costly and underutilized, limiting their effectiveness in DNs with multiple transformers and asynchronous peak loads. To address this, from the perspective of power supply companies, this study proposes a mobile soft open point (MSOP)-based approach to enhance the hosting capacity of EV charging stations. The method pre-installs a limited number of fast-access interfaces (FAIs) at candidate transformers and integrates a semi-rolling horizon optimization framework to gradually expand interface availability while scheduling MSOPs daily. An automatic peak period identification algorithm ensures optimization focuses on critical load periods. Case studies on a multi-feeder distribution system coupled with a realistic traffic network demonstrate that the proposed method effectively balances heterogeneous peak loads, matches limited interfaces with MSOPs, and enhances system-level hosting capacity. Compared with fixed SOP deployment, the strategy improves hosting capacity during peak periods while reducing construction costs. The results indicate that MSOPs provide a practical, flexible, and economically efficient solution for power supply companies to manage concentrated holiday charging surges in DNs. Full article

Accurate path travel time prediction is often hindered by sparse and heterogeneous traffic data. This paper proposes FusionODE-TT, a novel model designed to address these challenges by modeling traffic as a continuous-time process. The model features a Recurrent Neural Network encoder that processes multi-source time-series data to initialize a latent state vector, which then evolves over the prediction horizon using a Neural Ordinary Differential Equation (NODE). The core innovation is a guided fusion mechanism that leverages sparse but high-fidelity Automatic Vehicle Identification (AVI) data to apply strong, event-based corrections to the model’s continuous latent state, mitigating error accumulation in the prediction process. Experiments were conducted on a real-world dataset comprising AVI, GPS, and point sensor data from a major urban expressway. The experimental results demonstrate that the proposed model achieves superior accuracy, outperforming a suite of baseline models in terms of prediction accuracy and robustness. Furthermore, a comprehensive ablation study was performed to validate the efficacy of our design. The study quantitatively confirms that both the continuous-time dynamics modeled by the NODE and the guided fusion mechanism are essential components, each providing a significant and independent contribution to the model’s overall performance. Full article

Fine-grained modeling of driving styles is critical for decision making in autonomous driving. However, existing methods are constrained by the high cost of manual labeling and a lack of interpretability. This study proposes an unsupervised disentanglement framework based on a variational autoencoder (VAE), which, for the first time, enables the automatic extraction of interpretable driving style representations from car-following trajectories. The key innovations of this work are threefold: (1) a dual-decoder VAE architecture is designed, leveraging driver identity as a proxy label to guide the learning of the latent space; (2) self-dynamics and interaction dynamics features are decoupled, with an attention mechanism employed to quantify the influence of the lead vehicle; (3) a bidirectional interpretability verification framework is established between latent variables and trajectory behaviors. Evaluated on a car-following dataset comprising 25 drivers, the model achieves a Driver Identification accuracy of 98.88%. Mutual information analysis reveals the physical semantics encoded in major latent dimensions. For instance, latent dimension z₂₂ is strongly correlated with the minimum following distance and car-following efficiency. One-dimensional latent traversal further confirms that individual dimensions modulate specific behavioral traits: increasing z₂₂ improves safety margins by 18% but reduces efficiency by 23%, demonstrating that it encodes a trade-off between safety and efficiency. This work provides a controllable representation framework for driving style transfer in autonomous systems and offers a more granular approach for analyzing driver behavior in car-following scenarios, with potential for extension to broader driving contexts. Full article

The frequency of jellyfish blooms in marine ecosystems has been rising globally, attracting significant attention from the scientific community and the general public. Low-altitude remote sensing with Unmanned Aerial Vehicles (UAVs) offers a promising approach for rapid, large-scale, and automated image acquisition, making it an effective tool for jellyfish population monitoring. This study employed UAVs for extensive sea surface surveys, achieving quantitative monitoring of the spatial distribution of jellyfish and optimizing flight altitude through gradient experiments. We developed a “bell diameter measurement model” for estimating jellyfish bell diameters from aerial images and used the Mask R-CNN algorithm to identify and count jellyfish automatically. This method was tested in Qinglan Port, where we monitored Acromitus flagellatus populations from mid-April to mid-May 2021 and late May 2023. Our results show that the UAVs can monitor jellyfish with bell diameters of 5 cm or more, and the optimal flight height is 100–150 m. The bell diameter measurement model, defined as L = 0.0103 × H × N + 0.1409, showed no significant deviation from field measurements. Compared to visual identification by human experts, the automated method achieved high accuracy while reducing labor and time costs. Case analysis revealed that the abundance of A. flagellatus in Qinglan Port initially increased and then decreased from mid-April to mid-May 2021, displaying a distinct patchy distribution. During this period, the average bell diameter gradually increased from 15.0 ± 3.4 cm to 15.5 ± 4.3 cm, with observed sizes ranging from 8.2 to 24.5 cm. This study introduces a novel, efficient, and cost-effective UAV-based method for quantitative monitoring of large jellyfish populations in surface waters, with broad applicability. Full article

With the growing demand for natural and intelligent human–machine interaction in multilingual environments, automatic language identification (LID) has emerged as a crucial component in voice-enabled systems, particularly in the automotive domain. This study proposes an audio-based LID model that identifies the spoken language directly from voice input without requiring manual language selection. The model architecture leverages two types of feature extraction pipelines: a Variational Autoencoder (VAE) and a pre-trained Wav2Vec model, both used to obtain latent speech representations. These embeddings are then fed into a multi-layer perceptron (MLP)-based classifier to determine the speaker’s language among five target languages: Korean, Japanese, Chinese, Spanish, and French. The model is trained and evaluated using a dataset preprocessed into Mel-Frequency Cepstral Coefficients (MFCCs) and raw waveform inputs. Experimental results demonstrate the effectiveness of the proposed approach in achieving accurate and real-time language detection, with potential applications in in-vehicle systems, speech translation platforms, and multilingual voice assistants. By eliminating the need for predefined language settings, this work contributes to more seamless and user-friendly multilingual voice interaction systems. Full article

Although SAR systems can provide high-resolution aerial view images all-day, all-weather, the aspect and pose-sensitivity of the SAR target signatures, which defies the Gestalt perceptual principles, sets a frustrating performance upper bound for SAR Automatic Target Recognition (ATR). Therefore, we propose a network to support context-guided ATR by using aligned Electro-Optical (EO)-SAR image pairs. To realize EO-SAR image scene grammar alignment, the stable context features highly correlated to the parking patterns of the vehicle and aircraft targets are extracted from the EO images as prior knowledge, which is used to assist SAR-ATR. The proposed network consists of a Scene Recognition Module (SRM) and an instance-level Cross-modality ATR Module (CATRM). The SRM is based on a novel light-condition-driven adaptive EO-SAR decision weighting scheme, and the Outlier Exposure (OE) approach is employed for SRM training to realize Out-of-Distribution (OOD) scene detection. Once the scene depicted in the cut of interest is identified with the SRM, the image cut is sent to the CATRM for ATR. Considering that the EO-SAR images acquired from diverse observation angles often feature unbalanced quality, a novel class-incremental learning method based on the Context-Guided Re-Identification (ReID)-based Key-view (CGRID-Key) exemplar selection strategy is devised so that the network is capable of continuous learning in the open-world deployment environment. Vehicle ATR experimental results based on the UNICORN dataset, which consists of 360-degree EO-SAR images of an army base, show that the CGRID-Key exemplar strategy offers a classification accuracy 29.3% higher than the baseline model for the incremental vehicle category, SUV. Moreover, aircraft ATR experimental results based on the aligned EO-SAR images collected over several representative airports and the Arizona aircraft boneyard show that the proposed network achieves an F1 score of 0.987, which is 9% higher than YOLOv8. Full article

In the southeast of China, seasonal rainfall intensity is high, the distribution of mountains and hills is extensive, and many small-scale, shallow landslides frequently occur after consecutive seasons of heavy rainfall. High-precision automated identification systems can quickly pinpoint the scope of the disaster and help with important decisions like evacuating people, managing engineering, and assessing damage. Many people have designed systems for detecting such shallow landslides, but few have designed systems that combine high resolution, high automation, and real-time capability of landslide identification. Taking accuracy, automation, and real-time capability into account, we designed an automatic rainfall-induced shallow landslide detection system based on deep learning and Unmanned Aerial Vehicle (UAV) images. The system uses UAVs to capture high-resolution imagery, the U-Net (a U-shaped convolutional neural network) to combine multi-scale features, an adaptive edge enhancement loss function to improve landslide boundary identification, and the development of the “UAV Cruise Geological Hazard AI Identification System” software with an automated processing chain. The system integrates UAV-specific preprocessing and achieves a processing speed of 30 s per square kilometer. It was validated in Wanli District, Nanchang City, Jiangxi Province. The results show a Mean Intersection over Union (MIoU) of 90.7% and a Pixel Accuracy of 92.3%. Compared with traditional methods, the system significantly improves the accuracy of landslide detection. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article

Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency. Full article

This study proposes an automatic gear shift classification algorithm in M1 category vehicles using data acquired through the onboard diagnostic system (OBD II) and GPS. The proposed approach is based on the analysis of identification parameters (PIDs), such as manifold absolute pressure (MAP), revolutions per minute (RPM), vehicle speed (VSS), torque, power, stall times, and longitudinal dynamics, to determine the efficiency and behavior of the vehicle in each of its gears. In addition, the unsupervised K-means algorithm was implemented to analyze vehicle gear changes, identify driving patterns, and segment the data into meaningful groups. Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. After a thorough evaluation, the KNN (Fine KNN) model proved to be the most effective, achieving an accuracy of 99.7%, an error rate of 0.3%, a precision of 99.8%, a recall of 99.7%, and an F1-score of 99.8%, outperforming other models in terms of accuracy, robustness, and balance between metrics. A multiple linear regression model was developed to estimate instantaneous fuel consumption (in L/100 km) using the gear predicted by the KNN algorithm and other relevant variables. The model, built on over 66,000 valid observations, achieved an R² of 0.897 and a root mean square error (RMSE) of 2.06, indicating a strong fit. Results showed that higher gears (3, 4, and 5) are associated with lower fuel consumption. In contrast, a neutral gear presented the highest levels of consumption and variability, especially during prolonged idling periods in heavy traffic conditions. In future work, we propose integrating this algorithm into driver assistance systems (ADAS) and exploring its applicability in autonomous vehicles to enhance real-time decision making. Such integration could optimize gear shift timing based on dynamic factors like road conditions, traffic density, and driver behavior, ultimately contributing to improved fuel efficiency and overall vehicle performance. Full article

This paper presents a self-driving system for automatic parallel parking, integrating obstacle avoidance for enhanced safety. The vehicle platform employs three primary sensors—a web camera, a Zed depth camera, and LiDAR—to perceive its surroundings, including sidewalks and potential obstacles. By processing camera and LiDAR data, the system determines the vehicle’s position and assesses parking space availability, with LiDAR also aiding in malfunction detection. The system operates in three stages: parking space identification, path planning using geometric circles, and fine-tuning with fuzzy control if misalignment is detected. Experimental results, evaluated visually in a model-scale setup, confirm the system’s ability to achieve smooth and reliable parallel parking maneuvers. Quantitative performance metrics, such as precise parking accuracy or total execution time, were not recorded in this study but will be included in future work to further support the system’s effectiveness. Full article

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article