Search Results (330)

With the rapid development of hyperspectral applications using unmanned aerial vehicles (UAVs), the traditional assumption that ground objects exhibit Lambertian reflectance is no longer sufficient to meet the high-precision requirements for quantitative inversion and airborne hyperspectral data applications. Therefore, it is necessary to establish a hyperspectral bidirectional reflectance distribution function (BRDF) model suitable for the area of imaging. However, obtaining multi-angle information from UAV push-broom hyperspectral data is difficult. Achieving uniform push-broom imaging and flexibly acquiring multi-angle data is challenging due to spatial distortions, particularly under heightened roll or pitch angles, and the need for multiple flights; this extends acquisition time and exacerbates uneven illumination, introducing errors in BRDF model construction. To address these issues, we propose leveraging the advantages of multi-spectral cameras, such as their compact size, lightweight design, and high signal-to-noise ratio (SNR) to reconstruct hyperspectral multi-angle data. This approach enhances spectral resolution and the number of bands while mitigating spatial distortions and effectively captures the multi-angle characteristics of ground objects. In this study, we collected UAV hyperspectral multi-angle data, corresponding illumination information, and atmospheric parameter data, which can solve the problem of existing BRDF modeling not considering outdoor ambient illumination changes, as this limits modeling accuracy. Based on this dataset, we propose an improved Walthall model, considering illumination variation. Then, the radiance consistency of BRDF multi-angle data is effectively optimized, the error caused by illumination variation in BRDF modeling is reduced, and the accuracy of BRDF modeling is improved. In addition, we adopted Transformer for spectral reconstruction, increased the number of bands on the basis of spectral dimension enhancement, and conducted BRDF modeling based on the spectral reconstruction results. For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. In order to evaluate BRDF modeling and quantitative application potential from the reconstruction results, we conducted comparison and ablation experiments. Finally, we solved the problem of difficulty in obtaining multi-angle information due to the limitation of hyperspectral imaging equipment, and we provide a new solution for obtaining multi-angle features of objects with higher spectral resolution using low-cost imaging equipment. Full article

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models of the area or for generating orthomosaics from RGB, multispectral, hyperspectral, or thermal cameras. Based on a literature review, this paper provides comprehensive guidelines and best practices for executing such mapping flights. It addresses critical aspects of flight preparation and flight execution. Key considerations in flight preparation covered include sensor selection, flight height and GSD, flight speed, overlap settings, flight pattern, direction, and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration, and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume, and post-flight processing time. For reflectance and thermal measurements, BRDF issues also influence the correct setting. The formulated guidelines are based on literature consensus. However, the paper also identifies knowledge gaps for mapping flight settings, particularly in viewing angle pattern, flight direction, and thermal imaging in general. The guidelines aim to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications. Full article

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. Species, such as bats, that have slow life histories, characterized by extended lifespans and low reproductive rates, are particularly vulnerable to environmental changes, stochastic events, and human activities. An accurate assessment of productivity can improve parameters for population modelling and provide insights into species’ capacity to recover from population perturbations, yet data on reproductive output are often lacking. Recently, advances in drone technology have allowed for the development of a drone-based thermal remote sensing technique to accurately and precisely count the numbers of flying-foxes (Pteropus spp.) in their tree roosts. Here, we extend that method and use a drone-borne thermal camera flown at night to count the number of flying-fox pups that are left alone in the roost whilst their mothers are out foraging. We show that this is an effective method of estimating flying-fox productivity on a per-colony basis, in a standardized fashion, and at a relatively low cost. When combined with a day-time drone flight used to estimate the number of adults in a colony, this can also provide an estimate of female reproductive performance, which is important for assessments of population health. These estimates can be related to changes in local food availability and weather conditions (including extreme heat events) and enable us to determine, for the first time, the impacts of disturbances from site-specific management actions on flying-fox population trajectories. Full article

Nighttime semantic segmentation represents a challenging frontier in computer vision, made particularly difficult by severe low-light conditions, pronounced noise, and complex illumination patterns. These challenges intensify when dealing with Unmanned Aerial Vehicle (UAV) imagery, where varying camera angles and altitudes compound the difficulty. In this paper, we introduce NoctuDroneNet (Nocturnal UAV Drone Network, hereinafter referred to as NoctuDroneNet), a real-time segmentation model tailored specifically for nighttime UAV scenarios. Our approach integrates convolution-based global reasoning with training-only semantic alignment modules to effectively handle diverse and extreme nighttime conditions. We construct a new dataset, NUI-Night, focusing on low-illumination UAV scenes to rigorously evaluate performance under conditions rarely represented in standard benchmarks. Beyond NUI-Night, we assess NoctuDroneNet on the Varied Drone Dataset (VDD), a normal-illumination UAV dataset, demonstrating the model’s robustness and adaptability to varying flight domains despite the lack of large-scale low-light UAV benchmarks. Furthermore, evaluations on the Night-City dataset confirm its scalability and applicability to complex nighttime urban environments. NoctuDroneNet achieves state-of-the-art performance on NUI-Night, surpassing strong real-time baselines in both segmentation accuracy and speed. Qualitative analyses highlight its resilience to under-/over-exposure and small-object detection, underscoring its potential for real-world applications like UAV emergency landings under minimal illumination. Full article

Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal. Full article

Improvements in the navigation systems incorporated into unmanned aerial vehicles (UAVs) and new sensors are improving the quality of 3D mapping results. In this study, two flights were compared over the archaeological remains of the castle of Almenara, situated in Cuenca, Spain. We performed one with a DJI Phantom 4 (DJI Innovations Co., Ltd., Shenzhen, China) and the other with a Matrice 300 RTK (DJI Innovations Co., Ltd., Shenzhen, China) and the new Zenmuse P1 camera (45 mp, RGB sensor). With the help of the new software incorporated into the Zenmuse P1 camera gimbal, we could significantly reduce the flight time. We analysed the data obtained with these two UAVs and the built-in RGB sensors, comparing the flight time, the point cloud, and its resolution and obtaining a three-dimensional reconstruction of the castle. We describe the work and the flights carried out, depending on the type of UAV and its RTK positioning system. The improvement in the positioning system provides improvements in flight accuracy and data acquisition. We compared the results obtained in similar studies, and thanks to the advances in UAVs and their sensors with better resolution, we managed to reduce the data collection time and obtained 3D models with the same results as those from other types of sensors. The accuracies obtained with the RTK and the P1 camera are very high. The volumes calculated for a future archaeological excavation are precise, and the 3D models obtained by these means are excellent for the preservation of the cultural asset. These models can have various uses, such as the preservation of an asset of cultural interest, or even its dissemination and analysis in various studies. We propose to use this technology for similar studies of archaeological documentation and the three-dimensional reconstruction and visualisation of cultural heritage in virtual visits on the web. Full article

In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. This study proposes a reliable UAV top-down absolute positioning method (RTAPM) based on a monocular RGB camera that employs joint optimization and visual–inertial assistance. The proposed method employs a bird’s-eye view monocular RGB camera to estimate the UAV’s moving position. By comparing real-time aerial images with pre-existing satellite images of the flight area, utilizing components such as template geo-registration, UAV motion constraints, point–line image matching, and joint state estimation, a method is provided to substitute satellites and obtain short-term absolute positioning information of UAVs in challenging and dynamic environments. Based on two open-source datasets and real-time flight experimental tests, the method proposed in this study has significant advantages in positioning accuracy and system robustness over existing typical UAV absolute positioning methods, and it can temporarily replace GNSS for application in challenging environments such as disaster aid or forest rescue. Full article

During flight, aircraft cargo compartments are in a confined state. If a fire occurs, it will seriously affect flight safety. Therefore, fire detection systems must issue alarms within seconds of a fire breaking out, necessitating high real-time performance for aviation fire detection systems. In addressing the issue of fire target detection, the YOLO series models demonstrate superior performance in striking a balance between computational efficiency and recognition accuracy when compared with alternative models. Consequently, this paper opts to optimize the YOLO model. An enhanced version of the FDY-YOLO object detection algorithm is introduced in this paper for the purpose of instantaneous fire detection. Firstly, the FaB-C3 module, modified based on the FasterNet backbone network, replaces the C3 component in the YOLOv5 framework, significantly decreasing the computational burden of the algorithm. Secondly, the DySample module is used to replace the upsampling module and optimize the model’s ability to extract the features of small-scale flames or smoke in the early stages of a fire. We introduce RFID technology to manage the cameras that are capturing images. Finally, the model’s loss function is changed to the MPDIoU loss function, improving the model’s localization accuracy. Based on our self-constructed dataset, compared with the YOLOv5 model, FDY-YOLO achieves a 0.8% increase in mean average precision (mAP) while reducing the computational load by 40%. Full article

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance. Full article

This work aimed to understand how the energy released by short laser pulses can produce different effects in carbon targets with different allotropic states. The IR pulse laser ablation, operating at 1064 nm wavelength, 3 ns pulse duration, and 100 mJ pulse energy, has been used to irradiate different types of carbon targets in a high vacuum. Graphite, highly oriented pyrolytic graphite, glassy carbon, active carbon, and vegetable carbon have exhibited different mass densities and have been laser irradiated. Time-of-flight (TOF) measurements have permitted the evince of the maximum carbon ion acceleration in the generated plasma (of about 200 eV per charge state) and the maximum yield emission (96 μg/pulse in the case of vegetal carbon) along the direction normal to the irradiated surface. The ion energy analyzer measured the carbon charge states (four) and their energy distributions. Further plasma investigations have been performed using a fast CCD camera image and surface profiles of the generated craters to calculate the angular emission and the ablation yield for each type of target. The effects as a function of the target carbon density and binding energy have been highlighted. Possible applications for the generation of thin films and carbon nanoparticles are discussed. Full article

This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red–green–blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities. Notably, the YOLOv8 models, particularly YOLOv8n-seg, demonstrated superior processing efficiency, with an average time of 18.10 ms, significantly faster than the Detectron2 models, which exceeded 57 ms, while maintaining high performance with a mean intersection over union (IoU) of 0.944 and a Matthew’s correlation coefficient (MCC) of 0.957. A qualitative comparison of segmentation masks indicated that the YOLO models produced smoother and more accurate object boundaries, whereas Detectron2 showed jagged edges and under-segmentation. Statistical analyses, including ANOVA and Tukey’s HSD test (α = 0.05), confirmed the superior segmentation performance of models on depth maps over RGB images (p < 0.001). This study concludes by recommending the YOLOv8n-seg model for real-time 3D segmentation in precision agriculture, providing insights that can enhance volume estimation, yield prediction, and resource management practices. Full article

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection. Full article

In modern autonomous systems, measurement repeatability and precision are crucial for robust decision-making algorithms. Stereovision, which is widely used in safety applications, provides information about an object’s shape, orientation, and 3D localisation. The camera’s lens distortion is a common source of systematic measurement errors, which can be estimated and then eliminated or at least reduced using a suitable correction/calibration method. In this study, a set of cameras equipped with Basler lenses (C125-0618-5M F1.8 f6mm) and Sony IMX477R matrices are calibrated using a state-of-the-art Zhang–Duda–Frese method. The resulting distortion coefficients are used to correct the images. The calibrations are evaluated with the aid of two novel methods for lens distortion measurement. The first one is based on linear regression with images of a vertical and horizontal line pattern. Based on the evaluation tests, outlying cameras are eliminated from the test set by applying the 2$\sigma$ criterion. For the remaining cameras, the MSE was reduced up to 75.4 times, to 1.8 px−6.9 px. The second method is designed to evaluate the impact of lens distortion on stereovision applied to bird tracking around wind farms. A bird’s flight trajectory is synthetically generated to estimate changes in disparity and distance before and after calibration. The method shows that at the margins of the image, lens distortion might introduce errors into the object’s distance measurement of +17%−+20% for cameras with the same distortion and from −41% up to $+\infty$ for camera pairs with different lens distortions. These results highlight the importance of having well-calibrated cameras in systems that require precision, such as stereovision bird tracking in bird–turbine collision risk assessment systems. Full article

Runners achieve forward locomotion through diverse techniques. However, understanding the behavior of the involved kinematical variables remains incomplete, particularly when running overground and along an intensity spectrum. We aimed to characterize the biomechanical and physiological adaptations while running at low, moderate, heavy and severe intensities. Ten middle- and long-distance runners completed an incremental intermittent protocol of 800 m steps until exhaustion (1 km·h⁻¹ velocity increments and 30 s intervals) on an outdoor track field. Biomechanical data were captured using two high-resolution video cameras, and linear and angular kinematic variables were analyzed. With the intensity rise, a decrease in stride, step and contact times ([0.70–0.65], [0.35–0.33] and [0.42–0.37] s) and an increase in stride length and frequency and flight time ([3.13–3.52] m, [1.43–1.52] Hz and [0.28–0.29] s; p < 0.05) were observed, together with an increase in oxygen uptake and blood lactate concentrations ([54.7–67.6] mL∙kg⁻¹∙min⁻¹ and [3.1–10.2] mmol∙L⁻¹). A more flexed hip at initial contact and toe-off (152.02–149.36] and [165.70–163.64]) and knee at initial contact ([162.64–159.57]; p < 0.05) were also observed. A consistent gait pattern along each protocol step was exhibited, with minor changes without practical significance. Runners are constantly adapting their gait pattern, reflected in both biomechanical and physiological responses, both of which should be considered for better characterization. Full article

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R² = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season. Full article