UAV Technology Research Articles

Assessment of Difference in Structure from Motion for Stockpile Volume Estimation using UAV Approach

The software for processing the photogrammetry software is increasingly capable of producing a high-quality 3D model DEM, DSM, and DTM from an aerial photo and measuring the volume of material. Nowadays, UAV technology has been rapidly used for stockpile volume calculation for the mining and construction industry. The purpose of this study is to compare image processing for the stockpile volume calculation using three different software for the UAV with the conventional method GPS as a reference. The result obtained using GPS is processed using CIVIL3D and act as a base measurement of the stockpile volume to compare result obtain from photogrammetry software which is Pix4D, Agisoft, and SimActive’s Correlator3D. The analysis of this study compared 3D model for volume calculation from a photo image for each photogrammetry software selected and obtained the result volume calculation for photogrammetry with the accuracy achieving below 3% error from the referenced volume of the stockpile. 12 GCP were placed around the study area to test the difference between the numbers of GCP processing for the volume stockpile results. The image acquisition is taken at 10 meter altitude and 20 meter altitude using UAV Phantom 4 pro and set with Pix4Dcapture circular mission. The processing method is SfM in the 3 photogrammetric selected that have 4 types of processing for each software, 10 meter altitude and 20 meter altitude with 4 GCP and 12 GCP. 12 results of the 3D model and volume of the stockpile was obtained. As for the volume results, the Pix4D 10 meter 4 GCP result obtained the closest volume to the reference. The point cloud generated is also stable based on the cloud compare volume density result. Overall, the study can conclude that not only the altitude of flight affected, and the type of software used is affecting the number of point clouds generated but also the number of GCP.

UAV Target Detection Algorithm with Improved YOLOv7

The wide application of UAV technology in various fields makes UAV target detection crucial. In this study, we propose an improved algorithm based on YOLOv7 to enhance the performance and robustness of UAV target detection. We utilize YOLOv7 as the infrastructure and introduce BiFPN (Bi-directional Feature Pyramid Network) to enhance the feature fusion, while adding the GAM attention mechanism to the model, which is trained and evaluated using the VisDrone2019 dataset. The experimental results of this study show that the improved model achieves an average accuracy mAP value of 45.6%, which is 2.7% higher than the traditional model, and is able to detect and localize UAV targets more accurately.

Unmanned Aerial Vehicle (UAV) Photogrammetry for Heritage Building Documentation

Historic buildings are silent witnesses, bridging past, present, and future generations. The Sasaksaat Railway Station, which is inseparable from the 950-meter-long Sasaksaat Tunnel, is a treasure trove of historical heritage in West Java; it holds many stories and memories that are very closely related to the nation’s history, protected by Law Number 11 of 2010 concerning Cultural Heritage. This station is located in contoured land filled with dense vegetation, so a UAV photogrammetry method is needed in accordance with the character of the building and the area to be documented. The research documentation on these historical sites employs the UAV photogrammetry method, combining crosshatch flight plans, 60-degree tilt camera configurations, and making 3D BIM models using point cloud photogrammetry techniques. The research yielded impressive results, demonstrating the effectiveness of UAV Photogrammetry in accelerating the process of archiving historic buildings and their surroundings. The integration of this method significantly improves the documentation workflow, enabling comprehensive and detailed recording of the Sasak railway stations. This ANOVA test found no significant difference between manual size comparisons and photogrammetric results, even using direct geo-reference via UAV technology. The ease and reliability of this method approach can be recommended for low-cost historic building documentation activities.

A novel CNN gap layer for growth prediction of palm tree plantlings.

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

A comprehensive analysis of global patent landscape for recent R&D in agricultural drone technologies

The rapid development of information technology, along with advanced wireless communication technologies, has revolutionized the use of unmanned aerial vehicles (UAVs or drones) in many industries. In agriculture, due to the climate change and the growing global population, causing unprecedented demands and risks on food supplies, intelligent and automatic agricultural technologies are critical needs. UAVs offer a wide range of applications to transform traditional agricultural practices into Agriculture 4.0 that integrates advanced technology to optimize agricultural productivity, sustainability, and efficiency. To gain comprehensive insights into the current and future development of agricultural UAVs technologies, this research conducts extensive review and analysis of patents and non-patent literature in the field. By thoroughly examining the literature, the knowledge ontology of ag-UAV technologies is presented. Additionally, comprehensive macro- and micro-patent analyses identify the patenting trends and top tech-leaders for the key technologies related to agricultural drones. Moreover, utilizing regression modeling, technology maturity analysis, and technology-function matrix (TFM), the current and future R&D trends and the cold and hot spots of the technical innovations are identified. Through these detailed patent analyses, the state-of-the-art and potential advancements in agricultural UAV technologies are depicted, serving as crucial intelligence for R&D initiatives and IP strategies.

TOWARDS UAV ASSISTED MONITORING OF AQUATIC VEGETATION WITHIN LARGE RIVERS – THE MIDDLE DANUBE (SERBIA)

UAV technologies provide a time- and cost-efficient framework for a variety of environmental monitoring domains. It also increases data resolution and provides new insights into observed objects and phenomena, especially within the difficult-to-access and complex for monitoring aquatic habitats. The objective of this study was to develop UAV-based acquisition and GIS-based image processing guidelines for aquatic macrophyte detection and monitoring in large temperate rivers. According to the European standard CEN EN -14184:2014, the assessment of aquatic macrophytes should be performed using the transect approach. Large rivers, such as the Danube, represent an exception and should be evaluated using 1km transects. Therefore, seven transects of the Middle Danube in Serbia were simultaneously surveyed using traditional field methods and novel UAV technology. UAV images were acquired using RGB and multispectral cameras carried by a fixed-wing drone. The images were processed and orthomosaics were classified using Object Based Image Analysis (OBIA), to create digital GIS maps of the river transects. During the traditional monitoring approach, the relative abundance of 22 macrophyte species was recorded along the transects. Using the UAV technology and OBIA approach eight macrophyte classes were distinguished based on dominant macrophyte taxa or plant life form traits. Aquatic macrophytes were 'almost perfectly' distinguished from the orthomosaics, achieving a high classification accuracy of 96 % / 88 % / 0.84 for RGB and 94 % / 97 % / 0.95 Producers /Users accuracy/Kappa index for the multispectral approach. Individual macrophyte classes accuracy varied between 0.5 and 1 Kappa and were generally higher for the multispectral imagery approach. Although the resolution of the taxonomic data is lower, UAV monitoring provided the necessary spatial context of macrophytes distribution and absolute area occupied by macrophytes. It also provided information on the diversity and distribution of habitats along the river. Therefore, the UAV-assisted monitoring approach described in this study can be effectively integrated into macrophyte monitoring during large river expeditions such as the JDS.

Performance Comparison of YOLOv5 and YOLOv8 Architectures in Human Detection using Aerial Images

The development of UAV technology has reached the stage of implementing artificial intelligence, control, and sensing. Cameras as UAV data inputs are employed to ensure flight safety, search for missing persons, and disaster evacuation. Human detection using cameras while flying is the focus of this article. The application of human detection in pedestrian areas using aerial image data is used as the dataset in the deep learning input process. The architectures discussed in this study are YOLOv5 and YOLOv8. The precision, recall, and F1-score values are used as comparisons to evaluate the performance of these architectures. When both architecture performances are applied, YOLOv8 outperforms YOLOv5. The achieved performance of YOLOv8 is a precision of 84.62%, recall of 75.93%, and F1-score of 79.98%.

The development and application of UAV intelligent machine learning system based on artificial intelligence

In the rapid development of artificial intelligence technology, some scholars proposed to integrate artificial intelligence and UAV technology together, comprehensively improve the application technology level of UAV, and truly meet the needs of information collection and processing in different fields. According to the application of UAV technology means in recent years, whether in the military field or the civil field, UAV has a very broad prospect for development. Intelligent UAV will replace the traditional UAV in a comprehensive way. Therefore, Chinese scientific research field should strengthen the integration of artificial intelligence and UAV technology research and development efforts, so as to guide the sustainable and healthy development of our UAV cause. After understanding the advantages of artificial intelligence and UAV technology, this paper mainly explores the development and application of UAV intelligent machine learning system according to the existing UAV technical means and artificial intelligence algorithm, and takes the UAV intelligent machine learning system in the agricultural field as an example to effectively verify the reliability of the application algorithm.

A Review of The Latest Innovations in UAV Technology

A Review of The Latest Innovations in UAV Technology

Multi-UAV Aerial Mission Intelligent Assignment Algorithm Based on LightGBM Model

Abstract With the development of UAV technology, UAVs have the advantages of convenience, speed, and small size. However, when faced with a complex task environment, some traditional artificial intelligence algorithms will significantly increase computation time, and the algorithms that perform better in time performance often fail to adapt to the type and number of random tasks to be assigned. In this paper, we combine the Hungarian algorithm with improving the LightGBM model, simulate the implementation in various task scenarios, and verify that the improved LightGBM model completes the intelligent assignment of multiple UAVs without increasing time cost compared to the classical algorithm through comparative analysis. It is shown that the improved LightGBM model has an average time of 26.39s for 10 runs, which is higher than 16.91s for NSGA-II and 14.73s for PESA, but lower than 46.92s for SPEA2 and 50.01s for SPGA2. For a non-real-time assignment system, a better mission intelligence assignment at a certain time cost scheme is more in line with the system’s performance requirements. Therefore, the improved LightGBM model in this paper can obtain better solutions than the other four algorithms in solving the multi-objective multi-UAV task intelligence assignment problem, and the solution set coverage is larger than several other algorithms. This indicates that the algorithm studied in this paper is feasible in solving the multi-objective multi-UAV task intelligence assignment problem, and it also has some reference significance for solving the multi-objective problem and improving the coverage of the multi-objective solution set.

Preliminary weight estimation of canister based light foldable wing electric propulsion UAV, as a platform for swarm applications

Lots of disruptive UAV technologies are rising every day. Especially in the design process, under the prism of the upcoming swarm trend, a novelty touch to new foldable designs has started. In combination with electric propulsion systems, those platforms come in reality. Because of their size, the same design can be enlarged and reduced with minor changes at its subsystems. This simplifies the construction process and minimizes the costs. Due to their application range both, civilian and military platforms started to develop their own foldable UAVs, basically for swarm applications. In this study, the basic parameters of such a scalable design will be proposed. A literature survey will be presented, to determine the most common sizes of those platforms and the battery market availability. Afterwards, a mission profile will form the basis of the conceptual process for different dimensional UAVs. Two different methods about the weight estimation of the UAV will be examined. The goal of this multilateral examination is that; enable the designer to precede to an appropriate selection of subsystems of the UAVs, i.e., battery, autopilot and telemetry systems based on the weight estimation.

Blockchain based Agriculture Using the Application of UAV and Deep Learning Technique: Alexnet CNN

Due to the warm and humid environment of Bangladesh, it is highly exposed to occurring perpetuation of various viruses which cause diseases in crops. A huge number of crops are wasted because of these occurring diseases and it directly hurts the production rate and forces import of crops in bulkier amount. Unmanned aerial vehicle usage is one of the smart agriculture technologies being researched for agricultural applications (UAVs) in these days. UAV technology allows farmers to quickly gather information on field conditions by providing overhead images of their agricultural fields or even allowing them to zoom in on a particular area. Using UAV technology, farmers may identify specific areas that need immediate attention and perform the necessary agricultural improvements. Drones collect data that farmers can use to detect crop disease by applying deep learning algorithms to make long-term decisions about planting, land mapping, damage control, and other things. This research uses blockchain technology to establish connection between suppliers and customers by enabling information to be tracked throughout the supply chain and enhances food supply chain safety. It offers a secure method of broadcasting data, focusing on enhancement of supply chain management and prediction of crops which makes it possible to implement and deploy data-driven technologies for smart farming. The research uses UAVs as a means of collecting crop images, implements a prediction model using AlexNet CNN and analyses how it performs with a real Bangladeshi crop disease dataset to help farmers from excessive crop damage. Furthermore, the overall process is carried out using the Blockchain technology to enhance the existing supply chain management process.

Comparison of anti-interference ability between PID controller and ADRC controller in UAV operation at ocean

With the maturity of UAV technology, drones can carry different instruments in the air to help people complete their work more efficiently. However, different working environments also bring different challenges to UAV control systems. This paper mainly discusses the quadrotor UVA and compares the stability of the Proportional Integral Derivative (PID) controller and Active disturbance rejection controller (ADRC) under the disturbance of gusts at sea. The flight principle of the quadrotor and the dynamic model of the quadrotor will be discussed on this basis. Then the composition and mathematical formula of the PID and ADRC controllers are introduced and compared. In general, this paper focused on the anti-jamming ability of different controllers under the influence of gust, which shows that although the ADRC controller has a more complex system and tedious parameter adjustment process in comparison with the PID controller, it has excellent anti-gust interference ability and can better serve the offshore operation of UAV.

UAV Technology for Precision Weed Detection and Management

In India population increases rapidly day-by-day, therefore food demand also increases. Traditional farming is insufficient to meet these demands. Thus, Unmanned Aerial Vehicles (drones) were introduced, enabling Weed Management in less time, efficient use of herbicide and pesticide to bring sustainability to the environment and their resources. One of the most detrimental biotic variables in agriculture, weeds significantly reduces yields across the globe. Weed patches from crop fields can be identified by using drone sensors. Drone camera sensors can optimize weed in relation to leaf density, chlorophyll concentration and other plant canopy characteristics. The article emphasizes weed detection and management by the use of potential sensors of Unmanned Aerial Vehicles (drones) technology for better agriculture output.

Assessing the utilization of UAV technology for cadastre update applications in Turkey based on cost-benefit analysis

The aim of this study is to examine the use of UAVs from various perspectives in cadastral update studies and present the advantages and disadvantages of this method. The cadastral data obtained through direct fieldwork, the stereo model created by UAV images, and the orthophoto developed from UAV images were compared within the scope of the cadastre update study. Based on the data obtained from the UAV, it was observed that the detail points were identified more accurately and in a shorter time, saving 66.0% of the time and 75.7% of the cost.

Wide-Area and Long-Term Agricultural Sensing System Utilizing UAV and Wireless Technologies

Wide-Area and Long-Term Agricultural Sensing System Utilizing UAV and Wireless Technologies

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

SMOKE DETECTION ON CNN BASED VIDEO SURVEILLANCE SYSTEM

Forest fires are a serious problem that can cause extensive forest land and plantation areas to be damaged, this damage not only disrupts the habitat but the ecosystems in the forest, several studies have made an experiment to prevent forest fires, one of which is by using the help of electronic sensors installed in forest areas, this sensor works chemically by detecting heat or a change in the composition of the atmosphere present in the air and room temperature, from these changes the data is sent to the central station and a fire will be predicted, this method has a weakness including the number of sensors installed is very limited and does not allow it to be installed in a large area and the sensor may be damaged and lost as well as the use of a limited power source, causing readings to become disruptive and less than optimal. The solution to this problem is to use a UAV or drone technology which is felt to be very effective and quickly moves areas to scan a very large area, while the fire detection process uses an image processing method based on a computer vision algorithm, this method reads forest fire data and calculates the area directly. and can be used to detect the impact of damage caused, the accuracy of this image processing system depends on whether it is clear or not the reading of the data captured by the UAV predicts an accuracy of up to 80%.

Application of UAV in Fire Emergency Communication Support

Fire emergency communication is an important guarantee condition for the orderly and rapid development of fire emergency rescue work. However, the complexity of emergency rescue environment and the weakness of communication support force increase the difficulty of fire emergency communication support and affect the effect of communication support. Considering the importance and necessity of communication support in fire emergency rescue, as well as the gradual maturity of UAV technology, more and more fire emergency rescue work has introduced UAV technology, with the help of UAV to improve the support of emergency communication. This paper summarizes the practical application situation and advantages of UAV technology in fire emergency communication support, and elaborates the problems that need attention in the application of UAV in fire emergency communication support, so as to provide the corresponding reference for the application and development of UAV in fire emergency communication support.

UAV Technology for Source Recovery and Emergency Response

UAV Technology for Source Recovery and Emergency Response