Search Results (3,989)

This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study’s primary objective is to refine the model’s batch size for improved accuracy and efficiency in crop detection and classification. Using the Google Colaboratory platform, the YOLOv8 model was trained over various batch sizes (10, 20, 30, 40, 50, 60, 70, 80, and 90) to automatically identify the five different classes (sugarcane, banana trees, spinach, pepper, and weeds) present on the UAV images. The performance of the model was assessed using classification accuracy, precision, and recall with the aim of identifying the optimal batch size. The results indicate a substantial improvement in classifier performance from batch sizes of 10 up to 60, while significant dips and peaks were recorded at batch sizes 70 to 90. Based on the analysis of the obtained results, Batch size 60 emerged with the best overall performance for automatic crop detection and classification. Although the F1 score was moderate, the combination of high accuracy, precision, and recall makes it the most balanced option. However, Batch Size 80 also shows very high precision (98%) and balanced recall (84%), which is suitable if the primary focus is on achieving high precision. The findings demonstrate the robustness of YOLOv8 for automatic crop identification and classification in a mixed crop farm while highlighting the significant impact of tuning to the appropriate batch size on the model’s overall performance. Full article

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified. Full article

Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for insulator automatic defect inspection. Positioning insulators is an important prerequisite step for defect detection, and the accuracy of insulator positioning greatly affects defect detection. However, traditional horizontal detectors lose directional information and it is difficult to accurately locate tilted insulators. Although oriented detectors can predict detection boxes with rotation angles to solve this problem, these models are complex and difficult to apply to edge devices with limited computing power. This greatly limits the practical application of deep learning methods in insulator detection. To address these issues, we proposed a lightweight insulator oriented detector. First, we designed a lightweight insulator feature pyramid network (LIFPN). It can fuse features more efficiently while reducing the number of parameters. Second, we designed a more lightweight insulator oriented detection head (LIHead). It has less computational complexity and can predict rotated detection boxes. Third, we deployed the detector on edge devices and further improved its inference speed through TensorRT. Finally, a series of experiments demonstrated that our method could reduce the computational complexity of the detector by approximately 49 G and the number of parameters by approximately 30 M while ensuring almost no decrease in the detection accuracy. It can be easily deployed to edge devices and achieve a detection speed of 41.89 frames per second (FPS). Full article

Invasive knotweeds are rhizomatous and herbaceous perennial plants that pose significant ecological threats due to their aggressive growth and ability to outcompete native plants. Although detecting and identifying knotweeds is crucial for effective management, current ground-based survey methods are labor-intensive and limited to cover large and hard-to-access areas. This study was conducted to determine the optimum flight height of drones for aerial detection of knotweeds at different phenological stages and to develop automated detection of knotweeds on aerial images using the state-of-the-art Swin Transformer. The results of this study found that, at the vegetative stage, Japanese knotweed and giant knotweed were detectable at ≤35 m and ≤25 m, respectively, above the canopy using an RGB sensor. The flowers of the knotweeds were detectable at ≤20 m. Thermal and multispectral sensors were not able to detect any knotweed species. Swin Transformer achieved higher precision, recall, and accuracy in knotweed detection on aerial images acquired with drones and RGB sensors than conventional convolutional neural networks (CNNs). This study demonstrated the use of drones, sensors, and deep learning in revolutionizing invasive knotweed detection. Full article

This paper presents a single tree segmentation method applied to 3D point cloud data acquired with a LiDAR scanner mounted on an unmanned aerial vehicle (UAV). The method itself is based on clustering methods and graph theory and uses only the spatial properties of points. Firstly, the point cloud is reduced to clusters with DBSCAN. Those clusters are connected to a 3D graph, and then graph partitioning and further refinements are applied to obtain the final segments. Multiple datasets were acquired for two test sites in the Czech Republic which are covered by commercial forest to evaluate the influence of laser scanning parameters and forest characteristics on segmentation results. The accuracy of segmentation was compared with manual labels collected on top of the orthophoto image and reached between 82 and 93% depending on the test site and laser scanning parameters. Additionally, an area-based approach was employed for validation using field-measured data, where the distribution of tree heights in plots was analyzed. Full article

This paper introduces a comprehensive framework for generating obstacle-free flight paths for unmanned aerial vehicles (UAVs) in intricate 3D environments. The system leverages the Rapidly Exploring Random Tree (RRT) algorithm to design trajectories that effectively avoid collisions with structures of diverse shapes and sizes. Discussion revolves around the challenges encountered during development and the successful achievement of generating collision-free routes. While the system represents an initial iteration, it serves as a foundation for future projects aiming to refine and expand upon its capabilities. Future work includes simulation testing and integration into UAV missions for image acquisition and structure scanning. Additionally, considerations for swarm deployment and 3D reconstruction using various sensor combinations are outlined. This research contributes to the advancement of autonomous UAV navigation in real-world scenarios. Full article

Non-invasive concealed object detection, identification, and discrimination have been of interest to the research community for decades due to the needs to preserve infrastructures and artifacts, guarantee safe conditions for the detection and location of landmines, etc. A modern approach is based on the use of an unmanned aerial vehicle equipped with ground-penetrating radar, which has the advantage of not requiring direct contact with the ground. Moreover, high-resolution underground images are obtained by coherently combining measurements by using a synthetic aperture radar algorithm. Due to the complexity of the real scenario, numerical analyses have always been welcomed to provide almost real-time information to make the best use of the potential of such kinds of techniques. This paper proposes an analysis of the scattering from a zero-thickness perfectly electrically conducting disk buried in a lossy half-space surrounded by air and illuminated by a field generated by a Hertzian dipole located in the air. It is carried out by means of a generalized form of the analytically regularizing Helmholtz–Galerkin technique, introduced and successfully applied by the authors to analyze the plane-wave scattering from a disk and a holed plane in a homogeneous medium. As clearly shown in the numerical results, the proposed method is very effective and drastically outperforms the commercial software CST Microwave Studio 2023. Full article

The need for pedestrian target detection in uncrewed aerial vehicle (UAV) remote sensing images has become increasingly significant as the technology continues to evolve. UAVs equipped with high-resolution cameras can capture detailed imagery of various scenarios, making them ideal for monitoring and surveillance applications. Pedestrian detection is particularly crucial in scenarios such as traffic monitoring, security surveillance, and disaster response, where the safety and well-being of individuals are paramount. However, pedestrian detection in UAV remote sensing images poses several challenges. Firstly, the small size of pedestrians relative to the overall image, especially at higher altitudes, makes them difficult to detect. Secondly, the varying backgrounds and lighting conditions in remote sensing images can further complicate the task of detection. Traditional object detection methods often struggle to handle these complexities, resulting in decreased detection accuracy and increased false positives. Addressing the aforementioned concerns, this paper proposes a lightweight object detection model that integrates GhostNet and YOLOv5s. Building upon this foundation, we further introduce the SPD-Conv module to the model. With this addition, the aim is to preserve fine-grained features of the images during downsampling, thereby enhancing the model’s capability to recognize small-scale objects. Furthermore, the coordinate attention module is introduced to further improve the model’s recognition accuracy. In the proposed model, the number of parameters is successfully reduced to 4.77 M, compared with 7.01 M in YOLOv5s, representing a 32% reduction. The mean average precision (mAP) increased from 0.894 to 0.913, reflecting a 1.9% improvement. We have named the proposed model “GSC-YOLO”. This study holds significant importance in advancing the lightweighting of UAV target detection models and addressing the challenges associated with complex scene object detection. Full article

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management. Full article

The environment’s psychological impacts on humans have been long studied, but many questions remain unanswered. We conducted a micro-scale study to examine the relationships among the objective characteristics of urban environmental settings, people’s subjective perception of such settings, and the related psychological responses. We employed a geo-enabled survey tool to gather data on individuals’ perceptions of the immediate environment within their daily activity space. The psychological processes assessed included emotional and affective states such as perceived stress and happiness. The data points were mapped on a high-resolution aerial image, which was classified to derive quantitative properties to examine the dose-response relationship between environmental exposure and psychological responses. Our results showed negative correlations between the momentary stress level and the amount of environmental elements such as water, trees, and grass. Positive correlations were detected between stress level and the amount of parking lot and barren land, as well as the distance to buildings. In terms of perceived happiness, positive environmental factors included water, trees, and artificial surfaces, with all other elements having negative correlations. Most of the correlations examined were not strong correlations. This could be due to the significant differences in how individuals respond to environmental stimuli. Full article

Carrier-based Unmanned Aerial Vehicle (CUAV) landing is an extremely critical link in the overall chain of CUAV operations on ships. Vision-based landing location methods have advantages such as low cost and high accuracy. However, when an aircraft carrier is at sea, it may encounter complex weather conditions such as haze, which could lead to vision-based landing failures. This paper proposes a runway line recognition and localization method based on haze removal enhancement to solve this problem. Firstly, a haze removal algorithm using a multi-mechanism, multi-architecture network model is introduced. Compared with traditional algorithms, the proposed model not only consumes less GPU memory but also achieves superior image restoration results. Based on this, We employed the random sample consensus method to reduce the error in runway line localization. Additionally, extensive experiments conducted in the Airsim simulation environment have shown that our pipeline effectively addresses the issue of decreased detection accuracy of runway line detection algorithms in haze maritime conditions, improving the runway line localization accuracy by approximately 85%. Full article

The spread of natural fires is a complex issue, as its mathematical modeling needs to consider many parameters. Therefore, the results of such modeling always need to be validated by comparison with experimental measurements under real-world conditions. Remote sensing with the support of satellite or aerial sensors has long been used for this purpose. In this article, we focused on data collection with an unmanned aerial vehicle (UAV), which was used both for creating a digital surface model and for dynamic monitoring of the spread of controlled grassland fires in the visible spectrum. We subsequently tested the impact of various processing settings on the accuracy of the digital elevation model (DEM) and orthophotos, which are commonly used as a basis for analyzing fire spread. For the DEM generated from images taken during the final flight after the fire, deviations did not exceed 0.1 m compared to the reference model from LiDAR. Scale errors in the model with only approximal WGS84 exterior orientation parameters did not exceed a relative accuracy of 1:500, and possible deformations of the DEM up to 0.5 m in height had a minimal impact on determining the rate of fire spread, even with oblique images taken at an angle of 45°. The results of the experiments highlight the advantages of using low-cost SfM photogrammetry and provide an overview of potential issues encountered in measuring and performing photogrammetric processing of fire spread. Full article

Accurate target identification of UAV (Unmanned Aerial Vehicle)-captured images is a prerequisite for maritime rescue and maritime surveillance. However, UAV-captured images pose several challenges, such as complex maritime backgrounds, tiny targets, and crowded scenes. To reduce the impact of these challenges on target recognition, we propose an efficient maritime rescue network (EMR-YOLO) for recognizing images captured by UAVs. In the proposed network, the DRC2f (Dilated Reparam-based Channel-to-Pixel) module is first designed by the Dilated Reparam Block to effectively increase the receptive field, reduce the number of parameters, and improve feature extraction capability. Then, the ADOWN downsampling module is used to mitigate fine-grained information loss, thereby improving the efficiency and performance of the model. Finally, CASPPF (Coordinate Attention-based Spatial Pyramid Pooling Fast) is designed by fusing CA (Coordinate Attention) and SPPF (Spatial Pyramid Pooling Fast), which effectively enhances the feature representation and spatial information integration ability, making the model more accurate and robust when dealing with complex scenes. Experimental results on the AFO dataset show that, compared with the YOLOv8s network, the EMR-YOLO network improves the mAP (mean average precision) and mAP50 by 4.7% and 9.2%, respectively, while reducing the number of parameters and computation by 22.5% and 18.7%, respectively. Overall, the use of UAVs to capture images and deep learning for maritime target recognition for maritime rescue and surveillance improves rescue efficiency and safety. Full article

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (K_x, K_y) ranging between 0.01–0.1 radm⁻¹ and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R²) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms⁻¹. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis. Full article

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%. Full article