UAV-Based Remote Sensing Applications for Bridge Condition Assessment

Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning

Steel structures are susceptible to corrosion due to their exposure to the environment. Currently used non-destructive techniques require inspector involvement. Inaccessibility of the defective part may lead to unnoticed corrosion, allowing the corrosion to propagate and cause catastrophic structural failure over time. Autonomous corrosion detection is essential for mitigating these problems. This study investigated the effect of the type of encoder-decoder neural network and the training strategy that works the best to automate the segmentation of corroded pixels in visual images. Models using pre-trained DesnseNet121 and EfficientNetB7 backbones yielded 96.78% and 98.5% average pixel-level accuracy, respectively. Deeper EffiecientNetB7 performed the worst, with only 33% true-positive values, which was 58% less than ResNet34 and the original UNet. ResNet 34 successfully classified the corroded pixels, with 2.98% false positives, whereas the original UNet predicted 8.24% of the non-corroded pixels as corroded when tested on a specific set of images exclusive to the investigated training dataset. Deep networks were found to be better for transfer learning than full training, and a smaller dataset could be one of the reasons for performance degradation. Both fully trained conventional UNet and ResNet34 models were tested on some external images of different steel structures with different colors and types of corrosion, with the ResNet 34 backbone outperforming conventional UNet.

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.

Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants

Aerial infrared (IR) thermography has been implemented in recent years, proving to be a powerful and versatile technique for performing maintenance at photovoltaic (PV) plants. Its application speed and reliability using unmanned aerial vehicles (UAVs) or drones make it extremely interesting at large PV plants, due to the associated savings in time and costs. Ground-level thermographic inspection is slower and more costly to apply, although it does provide higher optical resolution, due to being conducted closer to the PV modules being inspected. Both techniques used in combination can improve the diagnosis. An IR thermography inspection strategy is proposed for PV plants based on two stages. The first stage of the inspection is aerial, enabling thermal faults to be detected and located quickly and reliably. The second stage of the inspection is done on the ground and applied only to the most relevant incidents revealed in the first stage. This inspection strategy was applied to a 100 kW PV plant, with an improved diagnosis verified via this procedure, as the ground-level inspection detects one-off thermal incidents from objects creating shade and from solar reflections. For PV modules with open circuits or open substrings, the use of one technique or another is immaterial.

Digital inventory of agricultural land plots in the Kemerovo Region

Cadastral and geodetic land works are expensive, which makes aerial photography extremely valuable for land traceability and inventory. The present research objective was to develop a new digital survey technology for registration of agricultural lands. We assessed the accuracy of the new method and evaluated its decision support options. The study featured the case of the Kemerovo Region (Kuzbass), Russia. The aerial survey took place in 2021 and involved 17 municipalities of the Kemerovo Region. The software and hardware complex included an unmanned aerial vehicle (UAV) and a module for aerial photography. Photogrammetric, cartometric, and satellite methods were used to define the coordinates of feature points. We developed new software (Sovhoz.avi) to perform the land inventory. The photogrammetric and cartographic methods proved efficient in determining the feature points and boundaries of land plots. They also appeared accurate enough for land inventory and decision support. The study updated the available land inventory data. About 30% of all land plots were recorded incorrectly; some plots marked as agricultural appeared to belong to the local forest reserves or urban territories. Incorrect data (1.64%) were excluded from the official inventory. The survey covered a total area of 41 000 ha and revealed 1700 illegally used land plots. The updated inventory of unused lands included 3825 new plots (163 400 ha), which can attract prospective investors. The results can be used by the local authorities to make land management decisions and identify illegal land use.

Machine Learning-Based Concrete Crack Depth Prediction Using Thermal Images Taken under Daylight Conditions

Concrete cracks can threaten the usability of structures and degrade the aesthetics of buildings. Furthermore, minor cracks can develop into large-scale cracks that may lead to structural failure when exposed to excessive external loads. In addition, the concrete crack width and depth should be precisely measured to investigate the effects of concrete cracks on the stability of structures. Thus, a nondestructive and noncontact testing method was introduced for detecting concrete crack depth using thermal images and machine learning. The thermal images of the cracked specimens were obtained using a constant test setup for several months under daylight conditions, which provided sufficient heat for measuring the temperature distributions of the specimens, with recording parameters such as air temperature, humidity, and illuminance. From the thermal images, the crack and surface temperatures were obtained depending on the crack widths and depths using the parameters. Four machine-learning algorithms (decision tree, extremely randomized tree, gradient boosting, and AdaBoost) were selected, and the results of crack depth prediction were compared to identify the best algorithm. In addition, data bias analysis using principal component analysis, singular value decomposition, and independent component analysis were conducted to evaluate the efficiency of machine learning.

Path Planning and Control of a UAV Fleet in Bridge Management Systems

Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided.

Impact of UAV Hardware Options on Bridge Inspection Mission Capabilities

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements.

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.

Non-Destructive Testing Applications for Steel Bridges

The growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent any failures caused by aging and environmental impacts, bridges require periodic inspections. This becomes even more critical due to climate change and its effect on bridges, especially in the coastal regions. Most of the inspections conducted incorporate the visual type of evaluation due to its simplicity. However, with the current developments in new technologies, there is a need for more advanced techniques of structural health monitoring (SHM) methods to be incorporated in the maintenance programs for more accurate and efficient surveys. In this paper, non-destructive testing (NDT) methods applicable to steel bridges are reviewed, with a focus on methods applicable to local damage detection. Moreover, the methodology, advantages and disadvantages, and up-to-date research on NDT methods are presented. Furthermore, the application of novel NDT techniques using innovative sensors, drones, and robots for the rapid and efficient assessment of damages on small and large scales is emphasized. This study is deemed necessary as it compiles in one place the available information regarding NDT methods for in-service steel bridges. Access to such information is critical for researchers who intend to work on new or improved NDT techniques.

Quality Evaluation of Digital Twins Generated Based on UAV Photogrammetry and TLS: Bridge Case Study

In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.