Search Results (447)

The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R² ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. Full article

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss. Full article

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R² = 0.62), outperformed models driven solely by spectral bands (R² = 0.33) or vegetation indices (R² = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m²). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. Full article

The stator of a flat wire motor is the core component of new energy vehicles. However, detecting quality defects in the coating process in real-time is a challenge. Moreover, the number of defects is large, and the pixels of a single defect are very few, which make it difficult to distinguish the defect features and make accurate detection more difficult. To solve this problem, this article proposes the YOLOv8s-DFJA network. The network is based on YOLOv8s, which uses DSFI-HEAD to replace the original detection head, realizing task alignment. It enhances joint features between the classification task and localization task and improves the ability of network detection. The LEFG module replaces the C2f module in the backbone of the YOLOv8s network that reduces the redundant parameters brought by the traditional BottleNeck structure. It also enhances the feature extraction and gradient flow ability to achieve the lightweight of the network. For this research, we produced our own dataset of stator coating quality regarding flat wire motors. Data augmentation technology (Gaussian noise, adjusting brightness, etc.) enriches the dataset, to a certain extent, which improves the robustness and generalization ability of YOLOv8s-DFJA. The experimental results show that in the performance of YOLOv8s-DFJA compared with YOLOv8s, the [email protected] index increased by 6.4%, the precision index increased by 1.1%, the recall index increased by 8.1%, the FPS index increased by 9.8FPS/s, and the parameters decreased by 3 Mb. Therefore, YOLOv8s-DFJA can be better realize the fast and accurate detection of the stator coating quality of flat wire motors. Full article

Traditional lettuce counting relies heavily on manual labor, which is laborious and time-consuming. In this study, a simple and efficient method for localization and counting lettuce is proposed, based only on lettuce field images acquired by an unmanned aerial vehicle (UAV) equipped with an RGB camera. In this method, a new lettuce counting model based on the weak supervised deep learning (DL) approach is developed, called LettuceNet. The LettuceNet network adopts a more lightweight design that relies only on point-level labeled images to train and accurately predict the number and location information of high-density lettuce (i.e., clusters of lettuce with small planting spacing, high leaf overlap, and unclear boundaries between adjacent plants). The proposed LettuceNet is thoroughly assessed in terms of localization and counting accuracy, model efficiency, and generalizability using the Shanghai Academy of Agricultural Sciences-Lettuce (SAAS-L) and the Global Wheat Head Detection (GWHD) datasets. The results demonstrate that LettuceNet achieves superior counting accuracy, localization, and efficiency when employing the enhanced MobileNetV2 as the backbone network. Specifically, the counting accuracy metrics, including mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (nRMSE), and coefficient of determination (R²), reach 2.4486, 4.0247, 0.0276, and 0.9933, respectively, and the F-Score for localization accuracy is an impressive 0.9791. Moreover, the LettuceNet is compared with other existing widely used plant counting methods including Multi-Column Convolutional Neural Network (MCNN), Dilated Convolutional Neural Networks (CSRNets), Scale Aggregation Network (SANet), TasselNet Version 2 (TasselNetV2), and Focal Inverse Distance Transform Maps (FIDTM). The results indicate that our proposed LettuceNet performs the best among all evaluated merits, with 13.27% higher R² and 72.83% lower nRMSE compared to the second most accurate SANet in terms of counting accuracy. In summary, the proposed LettuceNet has demonstrated great performance in the tasks of localization and counting of high-density lettuce, showing great potential for field application. Full article

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. Full article

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities. Full article

The effectiveness of road signs is hindered by obstructions, such as vegetation, mutual obstruction of signs, or the road alignment itself. The traditional evaluation of road sign recognition effectiveness is conducted through in-vehicle field surveys. However, this method has several drawbacks, including discontinuous identification, unclear positioning, incomplete coverage, and being time-consuming. Consequently, it is unable to effectively assess the recognition status of road signs at any arbitrary point within the road space. Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising, resampling, and three-dimensional model construction. Furthermore, a method for assessing the highway sign occlusion rate based on the picking algorithm was proposed. The algorithm was applied to an actual road environment, and the occlusion by other items was simulated. The results demonstrated the effectiveness of the method. This new method provides support for the fast and accurate calculation of road sign occlusion rates, which is of great importance for ensuring the safe traveling of vehicles. Full article

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments. Full article

In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios. Full article

Vietnam’s seaport system currently includes 298 ports with 588 wharves (a total length of approximately 92,275 m), which is vital in developing Vietnam’s marine economy. The piled pier, a type of wharf structure, is widely used and accounts for up to 90%, while the remaining 10% is made up of other types of wharf structures, such as gravity and sheet pile quay walls. Most wharves have been operating for over 10 years and some for even more than 50 years. Noticeably, wharves are highly vulnerable and degrade rapidly due to many factors, especially heavy load impacts and severe environmental conditions. Additionally, wharves have a higher risk of deterioration than other inland infrastructure, such as buildings and bridges. Consequently, determining a wharf’s health is an important task in maintaining normal working conditions, extending its lifecycle, and avoiding other severe damage that could lead to dangers to the safety of vehicles, facilities, and humans. Moreover, regulated quality inspections usually include only simple inspections, e.g., displacement, settlement, geometric height, and tilt; the visual inspection and determination of dimensions by simple length-measuring equipment; concrete strength testing by ultrasonic and rebound hammers; and the experimental identification of the chloride ion concentration, chloride diffusion coefficient, corrosion activity of rebar in concrete, and steel thickness. These testing methods often give local results depending on the number of test samples. Therefore, advanced diagnostic techniques for assessing the technical condition of piled piers need to be studied. The impact vibration test (IVT) is a powerful non-destructive evaluation method that indicates the overall health of structures, e.g., underground and foundation structures, according to official standards. Hence, the IVT is expected to help engineers detect the potential deterioration of overall structures. It is fundamental that, if a structure is degraded, its natural frequency will be affected. A structure’s health index and technical condition are determined based on this change. However, the IVT does not seem to be widely applied to piled piers, with no published standard; hence, controversial issues related to accuracy and reliability still remain. This motivates the present study to recommend an adjusted factor (equal to 1.16) for the health index (classified in official standards for other structures) through numerical and experimental approaches before officially applying the IVT method to piled piers. The current work focuses on the health index using the design natural frequency, which is more practical in common cases where previous historical data and the standard natural frequency are unavailable. This study also examines a huge number of influencing factors and situations through theoretical analysis, experience, and field experiments to propose an adjusted indicator. The results are achieved with several assumptions of damages, such as the degradation of materials and local damages to structural components. With the proposed adjusted indicator, the overall health of piled piers can be assessed quickly and accurately by IVT inspections in cases of incidents, accidents due to collisions, cargo falls during loading and unloading, or subsidence and erosion due to natural disasters, storms, and floods. Full article

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions. Full article

The main problems of forest parameter extraction and forest stand volume estimation using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology are the lack of precision in individual tree segmentation and the inability to directly obtain the diameter at breast height (DBH) parameter. To address such limitations, the study proposed an improved individual tree segmentation method combined with a DBH prediction model to obtain the tree height (H) and DBH for calculating the volume of trees, thus realizing the accurate estimation of forest stand volume from individual tree segmentation aspect. The method involves the following key steps: (1) The local maximum method with variable window combined with the Gaussian mixture model were used to detect the treetop position using the canopy height model for removing pits. (2) The measured tree DBH and H parameters of the sample trees were used to construct an optimal DBH-H prediction model. (3) The duality standing tree volume model was used to calculate the forest stand volume at the individual tree scale. The results showed that: (1) Individual tree segmentation based on the improved Gaussian mixture model with optimal accuracy, detection rate r, accuracy rate p, and composite score F were 89.10%, 95.21%, and 0.921, respectively. The coefficient of determination R² of the accuracy of the extracted tree height parameter was 0.88, and the root mean square error RMSE was 0.84 m. (2) The Weibull model had the optimal model fit for DBH-H with predicted DBH parameter accuracy, the R² and RMSE were 0.84 and 2.28 cm, respectively. (3) Using the correctly detected trees from the individual tree segmentation results combined with the duality standing tree volume model estimated the forest stand volume with an accuracy AE of 90.86%. In conclusion, using UAV-LiDAR technology, based on the individual tree segmentation method and the DBH-H model, it is possible to realize the estimation of forest stand volume at the individual tree scale, which helps to improve the estimation accuracy. Full article

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. Full article

In scenarios where the global navigation satellite system is unavailable, unmanned aerial vehicles (UAVs) can employ visual algorithms to process aerial images. These images are integrated with satellite maps and digital elevation models (DEMs) to achieve global localization. To address the localization challenge in unfamiliar areas devoid of prior data, an iterative computation-based localization framework is commonly used. This framework iteratively refines its calculations using multiple observations from a downward-facing camera to determine an accurate global location. To improve the rate of convergence for localization, we introduced an innovative observation model. We derived a terrain descriptor from the images captured by a forward-facing camera and integrated it as supplementary observation into a point-mass filter (PMF) framework to enhance the confidence of the observation likelihood distribution. Furthermore, within this framework, the methods for the truncation of the convolution kernel and that of the probability distribution were developed, thereby enhancing the computational efficiency and convergence rate, respectively. The performance of the algorithm was evaluated using real UAV flight sequences, a satellite map, and a DEM in an area measuring 7.7 km × 8 km. The results demonstrate that this method significantly accelerates the localization convergence during both takeoff and ascent phases as well as during cruise flight. Additionally, it increases localization accuracy and robustness in complex environments, such as areas with uneven terrain and ambiguous scenes. The method is applicable to the localization of UAVs in large-scale unknown scenarios, thereby enhancing the flight safety and mission execution capabilities of UAVs. Full article