Thermal UAV image super-resolution via bimodal image feature fusion

Unmanned aerial vehicles (UAVs) provide rich thermal imagery data crucial for vision-related tasks such as feature extraction, image enhancement, and data synthesis. Acquiring high-resolution (HR) thermal images for UAVs remains challenging due to sensor hardware constraints. Guided super-resolution (SR) methods leverage HR visible images to enhance thermal images reconstruction from low-resolution (LR) inputs. However, these methods demonstrate limitations in restoring fine grained textural details. To solve the problem, we propose a hierarchical cross-modal mamba with multi-head reconstruction network (HCM-Mamba) for UAV thermal images SR. The framework introduces a residual state-space block (RSSB) that directly models long-range spatial dependencies, enabling robust contextual correlation learning for sparse, small targets in complex aerial scenes. To alleviate the problem of modal differences between thermal and visible images, we propose a cross-modal integration framework, which includes a hierarchical cross-modal bridging encoder (HCB-Encoder) (interaction, refinement, and enhancement). The complementary features are hierarchically aligned and integrated through a dual attention mechanism and multi-scale aggregation. In order to address the problems of structure distortion and detail loss in traditional end-to-end architectures, we propose a multi head collaborative reconstruction mechanism that ensures coherent optimization of global structure and local texture while decoupling the feature learning process. Extensive experiments have shown that HCM-Mamba has achieved significant improvements in multiple evaluation metrics, maintaining structure integrity, and restoring perceived real details under real-world UAV imaging conditions.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

This study empirically evaluated the effects of unmanned aerial vehicles (UAVs) applications and acceptable cost on intention to use UAVs in maritime shipping contexts. Factor analysis was employed to identify key UAVs applications (i.e. pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security), acceptable UAV cost, and intention to use UAVs. Survey data were collected from 201 respondents working for a maritime port corporation, shipping company, agency, and stevedoring company, and then hierarchical regression analysis was conducted to test research hypotheses and examine the effects of UAVs applications and acceptable cost dimensions on intention to use UAVs. The results suggested that the pollution forensics, supervision and service, search and rescue, humanitarian and emergency delivery, and safety and security dimensions positively affected intention to use UAVs. The study findings also revealed negative associations among seniority, turnover, and intention to use UAVs. Theoretical contributions and managerial implications are proposed to assist maritime port bureaus, corporations, and operating practice design in remaining competitive and efficient.

<p>In this paper, the performance of a 5-level cascaded H-bridge inverter in unmanned aerial vehicle (UAV) applications is analized to identify, at the converter design stage, the better device choice depending on different UAV operation scenarios. Considering that regardless of the specific application there are some typical operations, such as take-off, climb, land, cruise, and potential recurring climbs and descents, the results can support the choice by considering the typical working conditions of the specific application where the UAV would be used. The results have been obtained by simulating the H-bridge inverter considering the circuit models of insulated-gate bipolar transistors (IGBTs), GaN high-electron-mobility transistors (HEMTs), and Si and SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) provided by manufacturers. The study has highlighted that the choice of the device depends on the UAV usage, switching frequency, and load conditions. More specifically, considering the typical devices and systems costs in the case of a selective harmonic elimination procedure operating at the fundamental switching frequency, the Si devices should be used. Moreover, the preference for using IGBTs or Si MOSFETs depends on the typical working conditions of the UAV application. In the case of phase-shift carrier modulation technique, at 4 kHz the MOSFET is the best device and the choice between Si and SiC devices depends on the UAV application's main operation scenarios. At 20 kHz the SiC MOSFET is the best device, while at higher frequencies the GaN HEMT cost should be faced to take advantage of its best performance.</p>

High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard

Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography

Unmanned aerial vehicles (UAVs) are becoming an increasingly important tool for wildlife surveys and equipping UAVs with thermal imaging cameras could make these surveys even more effective. In my thesis I examined the feasibility of using a UAV equipped with a thermal imaging camera to conduct mesocarnivore surveys, search for duck nests built over water, and conduct duck brood surveys in southern Manitoba. For my first objective, I conducted nighttime mesocarnivore surveys with the UAV and thermal camera. I used a modified point-count survey from six waypoints and surveyed 29.5 ha in each replicate. I conducted a total of 200 flights over 53 survey nights during which I detected 32 mesocarnivores of eight different species. The UAV and thermal camera were effective at locating mesocarnivores, however given the large home ranges of mesocarnivores, my surveys should be considered estimates of minimum abundance and not a population census. For my second objective I conducted a two-part survey: 1) I evaluated the effectiveness of a UAV and thermal camera to locate duck nests relative to traditional surveys, and 2) tested the hypothesis that technician visits to nests may influence predation rates. Over the course of my 1st study the UAV located a total of 47 nests that were not located by technicians, however, the technicians located 164 nests missed by the UAV, and both survey methods located 71 of the same nests. There was also no difference in survival rates for nests monitored with the UAV versus those monitored by technicians. Though the UAV completed surveys faster than technicians, the usefulness of this technology was limited, because the UAV has a relatively low detection rates. My third objective was to evaluate the efficacy of using a UAV and thermal camera to conduct brood surveys. In 2018 and 2019, the UAV and thermal camera located a total of 1569 broods, compared to 666 located by ground technicians, and had higher detection rates (0.48 vs. 0.20). The UAV reliably located twice as many broods as ground technicians and completed surveys four times faster, indicating this technology has great utility for waterfowl biologists.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

With the maturation of remote sensing, the applications of small unmanned aerial vehicles are rapidly expanding. Efficient image object detection algorithms have become crucial for information extraction in unmanned aerial vehicles. To meet this demand, an improved YOLOv5s algorithm was developed and deployed within a multi-processor system to optimize the performance of object detection in high-resolution remote sensing images captured by small unmanned aerial vehicles. Through adjustments to the structure and parameters of YOLOv5s, the algorithm was enhanced to improve object recognition capabilities in high-resolution remote sensing imagery. Experimental results demonstrated that the improved YOLOv5s (I-YOLOv5s) algorithm effectively mitigates interference from shadows and other external factors, enabling precise identification of objects. During training, I-YOLOv5s exhibited faster convergence, reaching optimal status after approximately 176 iterations. In performance evaluation, the algorithm achieved F1 and Recall values of 0.92 and 0.94, respectively, significantly outperforming single-shot multibox detectors. I-YOLOv5s attained a maximum average precision of 0.96, markedly higher than comparative algorithms, with its Loss value reduced to a mere 0.06. The introduction of this enhanced algorithm not only enhances the accuracy and efficiency of object detection but also profoundly advances the further application of unmanned aerial vehicles in fields such as environmental monitoring, traffic management, and disaster assessment.

As part of our recent research to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicle (UAV) applications, we investigated the potential of carrier-phase Global Navigation Satellite System (GNSS) for attitude determination and control of small size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro- Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD)sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

This study presents an experimental investigation of blade-shaped riblets for drag reduction in unmanned aerial vehicle (UAV) applications. UAVs have gained significant attention since they can perform various missions, including surveillance, reconnaissance, and package delivery. However, their aerodynamic performance, specifically the high drag associated with their exposed surfaces, remains a key challenge for enhancing their efficiency and extending their flight endurance. To address this issue, riblet geometries are proposed as a potential solution, which can reduce the turbulent skin friction drag by up to 8%. The experimental investigation involves wind tunnel testing of blade-shaped riblets, with various spacing-to-height (s/h) ratios and constant groove cross-sectional area (Ag). The riblets are designed for application on the wing, empennage, and fuselage surfaces of a UAV. The investigations are performed on a flat plate for various flow conditions, including different freestream velocities, to evaluate the drag reduction effectiveness of the riblet configuration. The drag force is measured using a force balance system and flow visualization techniques are employed to assess the position where the boundary layer has transitioned to fully turbulent. The results demonstrate the drag-reducing effect of blade-shaped and trapezoidal riblets and the different performances observed for the various s/h ratios. The cases with s/h=1 result in the smallest drag coefficients, while the cases with s/h=2 have significantly increased drag values, compared to the smooth flat plate, due to the increased wetted surface area. These findings highlight the potential of riblets as an effective drag-reduction technique for UAV applications, enabling increased endurance and/or enhanced payload capacity.

Aerial inspection of solar modules is becoming increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. In this paper, we explore and evaluate the use of computer vision and deep learning methods for automating the analysis of fault detection and classification in large scale photovoltaic module installations. We use convolutional neural networks to analyze thermal and visible color images acquired by cameras mounted on unmanned aerial vehicles. We generate composite images by overlaying the thermal and visible images to investigate improvements in detection accuracy of faint features related to faults on modules. Our main goal is to evaluate whether image processing with multi-wavelength composite images can improve both the detection and the classification performance compared to using thermal images alone. The hypothesis is that fusion of images acquired at different wavelengths (i.e., thermal infrared, red, green, and blue visible ranges) would enhance the multi-wavelength representation of faults and thus their histogram feature signatures. The results showed a successful detection and localization of faint fault features using composite images. However, the classification of the fault categories did not show significant improvements and needs continued investigation. This research represents a step towards the design of robust automated methods to improve fault detection from airborne images. Further work is still necessary to reach a classification accuracy comparable with the performance of human experts.

Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint

The availability, cost, and applicability of Unmanned Aerial Vehicles (UAVs) are rapidly altering the way we perceive the landscape, can access sites, and model the landscape as it unfolds through remote sensing technologies. It falls to present and future researchers to take advantage of this and use the technology to improve our understanding of our surroundings. One way that geomorphologists would be able to take advantage of the improvements in technology is to use the UAV to map soil processes, in particular erosion. The current methods and technology for mapping gully erosion do not look at the smaller features but rather the landscape as a whole. The purpose of this research was to assess the prospects of UAV technology mapping gully erosion. Specifically, the study investigates the application of the UAV imagery to other technologies or methods in mapping gully erosion activities. This was achieved by testing the multiple available modelling software, by mapping objects of known shapes and volumes to determine which software produced results comparable to that of the known shapes. Tests were conducted to determine if this software and UAV combination would have the ability to map and 3D render soil erosion, thus allowing researchers to verify if this technique could be implemented into mapping gully systems. From these tests, it was found the UAV was able to improve the resolution to that of the available methods. Obtaining a resolution of 0.03m would allow geomorphologists to map and situate the soil erosional landscapes and map the test objects. The software package AgiSoft MetaShape had the highest accuracy and reliability in mapping various objects making it ideal in the mapping of smaller erosional features. Multiple erosional landscapes were mapped using a UAV and the attached RGB sensor and a second UAV with a multi-spectral sensor and AgiSoft MetaShape software. Both sensors mapped plant health, capturing plant health with band widths related to each sensor. Ideally the multi-spectral sensors were seen to be more versatile, however the RGB sensor can be useful. The combination of both UAVs created a 3D-rendered model, and the resulting data was useful in determining the potential future areas of erosion and existing areas of high erosion risk. The multi-sensor camera allowed the user to determine micro-features that could potentially become erosional areas in the future as it identified areas with a concentration in water flow. In conclusion, the UAV is a useful spatial tool to monitor, measure and manage soil erosion and gully mapping. Iqoqa. Ukusetshenziswa kweUAV kwenza izinguquko ngokushesha endleleni okubukwa ngayo izwe (landscape) kanti kukubacwaningi ukuthi sithathe ithuba lokusebenzisa lensizakusebenza ngendlela ezoba wusizo ukuze sibe nokuqonda okungcono kolwazi olukhona. Lobuchwepheshe bungasetshenziswa ukubona ukuqopha ukunyakaza komhlabathi ngoguguleka kwemihosha . Inhloso kube wukuhlola ukusetshenziswa kwe UAV ukubona ukuthi iyakwazi yini ukukhiqiza izithombe ezithathwe ngobuchwepheshe basemoyeni obusezingeni eliphezulu. Inhloso yenzekie ngokuthi kwenziwe ukuhlola okunhlobonhlobo kwimodelling software. Lokhu kwenziwe ngezimpawu zebalazwe zomumo owaziwayo kanye nezinga lomsindo, ukuqonda ukuthi ilona luphi uhlelokusebenza olusebenzayo ngezinga lomsindo oseduze . Ukuhlolwa kwenziwe ukubona ukuthi lenhlanganisela yohlelokusebenza ne UAV ingakwazi yini ukuveza ukuguguleka kwenhlabathi igugulwa ngebalalazwe nemodli ye3D kanye nokuqinisekisa ukuthi lobu buchwepheshe bungasetshenziswa ebalazweni lwemihosha kanye nemodeli yekhompyutha eyaziwa nge 3D computer models of gullies. Kulokhu kuhlola kutholakale ukuthi iUAV iyakwazi ukuthuthukisa ukuhleleka kohlelo okukhona. Ukuthola ukuhleleka okungango 0.03 m okungavumela igeomorphologist ukungena ebalazweni kuphinde kuveze ukuguguleka komhlabathi. Izinjongo eziwusayizi owaziwayo zifakwe ebalazweni, okuzale iAgiSoft MetaShape okube sezingeni eliphezulu lokuqagula kanye nokwethembeka ekufakeni ebalazweni izinto ezahlukahlukene . Imiphumela yemininingo ibe wusizo ukuqonda izindawo ezingaguguleka ngengomuso kanye nezindawo ezikhona ezisethubeni lobungozi bokuguguleka .Ikhamera eyimulti sensor yenze umcwaningi ukuthi abone izimpawu ezaziwa ngemicro features ezingaba sethubeni lokuba sendaweni engaguguleka ngengomuso njengoba zikhombisa ukuba nenkinga yobuningi kwamanzi agelezayo. Ukuphetha, i-UAV okwamanje iyinsizakusebenza ewusizo engasetshenziswa ocwaningweni lwegeomorphologists futhi ishibhile kunezinye izinsizakusebenza ezifana nemanned vehicle noma isatellite.

There are many applications of Unmanned Aerial Vehicle (UAV) networks where all UAVs belong to one organization or a person. In such applications, the number of UAVs that can be employed is limited due to the cost factor. Local Area Networks of Drones (LoDs) have been introduced recently for such purposes. This paper presents methods of determining the minimum number of UAVs to perform tasks in four basic categories of LoD applications: completing a single task at a time, completing multiple tasks simultaneously, monitoring the entire area using a single UAV branch and monitoring the entire area simultaneously using multiple branches of UAVs. Completing one task at a time always requires a minimum number of UAVs and increasing the number of simultaneous tasks performed always increases the number of UAVs required for any application.