A Review of Developments and Challenges of Preflight Preparation for Data Collection of UAV-based Infrastructure Inspection

A significant portion of the vital infrastructure supporting modern dynamic society, including bridges, railways, tunnels, dams, buildings, power plants, and highways, was constructed decades ago and it is important to ensure the safety, resilience, and efficiency of these critical infrastructures. As an example, according to the report of the American Road and Transportation Builders Association (ARTBA) which is based on data from the Federal Highway Administration (FHWA) National Bridge Inventory (NBI) for 2022, from a total of more than 617,000 bridges across the United States, 36% of them (near 224,000) need repair work and more than 43,500 bridges are rated in poor condition and classified as structurally deficient. Due to these statistics, ARTBA estimated the cost of identified repairs for all 224,000 bridges, is approximately $260 billion [1]. These reports and statistics indicate the importance of monitoring, maintenance, and rehabilitation of aging civil infrastructures.

Infrastructure inspection is one of the important aspects and a key to identifying damage, preventing potential hazards, and optimizing performance. Civil infrastructure condition assessment is carried out by utilizing data from inspection/or monitoring. Traditional methods to evaluate the condition of civil infrastructure usually is done by visual inspection by the inspectors considering the related criteria (e.g., NBI standards for the bridges [2,3]. However, these methods for infrastructure inspection can be timeconsuming, expensive, imprecise, and unsafe [4]. In recent years, to overcome these challenges, using Unmanned Aerial Vehicles (UAVs) also known as drones, has become one of the solutions, and it has attracted a lot of attention in civil engineering [5,6]. Due to UAV’s key benefits, such as enhanced safety, cost efficiency, time efficiency, and easy repeatability, they are now widely used for civil infrastructure inspection and remote sensing [7].

As with most emerging technologies, challenges for UAVbased inspection remain, such as image distortion, massive data, image matching, lack of access to some parts of the infrastructure, instability of the UAV, vibration effects, etc. [6]. Recent advances in computer vision and image processing techniques, especially developments in Artificial Intelligence (AI) would be beneficial for UAV-based infrastructure inspection in different damage detection aspects. However, these methods heavily rely on the quality of collected data, and inaccuracies may arise without highquality data. In this context, ensuring data quality is crucial for the success and reliability of UAV-based inspections. To harness the full potential of data processing techniques and enhance the overall efficiency of UAVs in infrastructure inspection, it is crucial to investigate pre-flight preparation and meticulous data collection to ensure high data quality. Regarding the importance of data quality and data collection, this study focuses on the recent developments and the challenges related to the pre-flight preparation for the data collection phase. For this purpose, the pre-flight preparation for the data collection phase is divided into two different categories: hardware selection and flight planning. The remainder of the paper is structured as follows: in section 2, developments and challenges of the hardware selection including UAV selection, payload selection, and camera calibration are discussed; Also, developments and challenges for flight planning are discussed in section 3, and section 4 delivers the conclusions of this paper.

Before discussing the hardware selection, it is important to consider and define the inspection purpose. Without considering the purpose of inspection, suitable hardware may not be chosen and consequently, it may lead to some issues in other steps such as low data quality or inappropriate data collection. Five different purposes are defined in this study: crack detection, delamination, fatigue, 3D modeling, and corrosion. These purposes of inspection are discussed briefly below.

a. Crack Detection: Most of the studies mentioned crack detection as the basic and major application of infrastructure inspection using UAVs [8]. There are two primary steps in the image-based surface crack assessment method. Crack detection comes first, intending to remove noise and extract cracked objects from the images. The extraction of crack edges and the calculation of crack characteristics, such as crack width and length, make up the second stage of crack assessment [9]. For this purpose, mostly RGB cameras are used to find cracks in the surface of bridges, tunnels, and other structures. Using optical cameras, the UAVs can take high-quality pictures from difficultto- reach regions [10].

b. Delamination: Deck delamination, also known as horizontal debonding in the deck’s subsurface, is frequently a sign that the deck reinforcement has deteriorated due to corrosion [11]. The shape and depth of delamination, environmental factors like air temperature and solar intensity, which introduce feature variation of the same delamination, and surface textures like cracks, color differences, patching, and road painting, which add external noise, are the current challenges for the purpose of delamination profiling through thermography [12].

c. Fatigue: Fatigue cracks can have lengths less than 7 mm and diameters as small as 0.1 mm, and they are exceedingly difficult to discern. Inaccessible areas such as huge cross frames, welded stiffeners, or other complex geometries are typically where fatigue cracks develop in the superstructure. Commonly, RGB and IRT cameras are utilized to identify fatigue cracks. The effectiveness of UAV-based fatigue crack detection is greatly influenced by the platform that is used, the environment, and the lighting [13].

d. 3D Modeling: Inspectors can view geometric data, such as damage location, and surface condition, such as damage kind and amount, by using 3D models of the structures, which provide a base from which damage information can be compared. To create 3D models, RGB cameras and LiDAR sensors can be used [14]. Photogrammetry creates 3D points from a set of 2D photos collected from various angles and positions all around the structure, as opposed to LiDAR, which often contains more 3D points. Photogrammetry has a higher processing cost and lower accuracy than LiDAR because it compares image attributes to build the 3D points. However, UAV-based LiDAR systems need expensive LiDAR sensors and GPS systems, which reduce battery life by adding more payload to the system, whereas photogrammetry merely needs an optical sensor [15].

e. Corrosion: Due to the widespread usage of metals in construction infrastructure, corrosion is a constant risk. A positive charge is released during the electrochemical process of corrosion, which results in the formation of a stable compound. Despite some corrosion on the underlying metal components, such as the steel reinforcement used in bridge concrete, the surface of steel bridges experiences a great deal of corrosion [16]. The most popular cameras for detecting corrosion are RGB and IRT cameras. Although infrared thermography is a promising technique for measuring, mapping, and detecting corrosion, more study is required before it can be used perfectly in the field [17-19].

As mentioned above, different kinds of infrastructure inspections require different tools and considerations depending on the inspection’s goal. The choice of appropriate equipment is the next stage, and it is covered below. The typical UAV is composed of a frame, motors, control unit, onboard sensors, communication system, and power supply. Many UAVs display a dual tube substructure to make it easier to mount various payloads [8]. Fixedwing, rotorcraft, multi-rotor drones, and hybrid vertical take-off and landing (VTOL) vehicles are the four common UAV designs [20]. Figure 1 shows an example of these different types of UAVs and they are compared and discussed below. When it comes to operating characteristics and different airframes, fixed-wing drones are like conventional aircraft, they are usually bigger sized than other types and they can efficiently cover large distances. Similar to helicopters, rotorcraft UAVs have revolving propellers attached to the aircraft frame. Single-rotor drones are small-sized helicopter, which runs on gas or electricity. The multi-rotor UAV is a version of this type that has numerous propellers extending from the main body to increase the drone’s flight. They are mostly used for common applications like aerial photography, aerial video surveillance, etc. Other kinds include the Tricopter (with three rotors), Quadcopter (with four rotors), Hexacopter (with six rotors), and Octocopter (with eight rotors). Multi-rotor UAVs can perform complex 3D mapping since they are quick and stable. Rotorcrafts, on the other hand, are more difficult to fly manually than multi-rotors since they are easier to control, have more lift, and have a backup plan in case of motor failure. Measurement Errors can be caused by the UAV’s instability or increasing mobility. Finally, hybrid VTOL UAVs combine fixed-wing and multirotor designs, where the plane is propelled vertically before flying horizontally [10,21-23].

The choice of the appropriate UAV platform and sensors has proven to be a difficult problem due to UAV performance requirements related to flights close to the structure (e.g., turbulent flow characteristics around the bridge) and terrain characteristics (e.g., surface roughness, temperature, and humidity) [24]. This includes positioning and maneuvering the UAV around or under structures (operations prohibited by GPS) [25,26] and the stability of the platform in windy situations, where turbulence and other aerodynamic phenomena cause unpredictable wind effects [27,28]. One another major limitation of UAVs is the limited battery capacity or flight time. Moreover, it is not reasonable to increase battery sizes more than current battery sizes because it will affect the payload capacity and maneuverability of the UAV. A solution for this problem could be using wireless power transfer but still more investigation in this area seems crucial [29,30]. The main difficulty in maximizing the UAV for infrastructure inspections is striking a balance between payload capacity and compatibility, endurance, vehicle stability, navigational capabilities, and cost. These parameters play a pivotal role in UAV platform selection. By considering these parameters and inspection purposes, infrastructure inspections can be conducted with enhanced efficiency and data accuracy. A vehicle with a stabilizing gimbal that can change the camera pointing angle to any vertical angle, a camera with optical zoom for capturing high-resolution imagery while at a safe standoff distance, a vertical takeoff and landing capability, and the capacity to hover in place during the flight are examples of parameters and vehicle characteristics appropriate for infrastructure inspections [31].

The purpose of an infrastructure inspection mission is to collect data using designated sensors. UAV and payload selection should be done simultaneously because some UAVs come with fixed payloads while others can be customized with different payloads. Also, there are some UAVs that have fixed payloads, but other payloads can be added too. Therefore, considering the inspection purpose, UAV and payload selection are relevant to each other, and as mentioned before, if the UAV is customizable, payload capacity and compatibility of the drone are effective parameters for payload selection too [24]. Generally, the choice of the best sensors for infrastructure inspection depends on several factors, including cost, flight time, mission objectives, the UAV’s payload capacity and compatibility, the payload’s controllability, and navigational needs [31]. Although in the infrastructure inspection purposes section, the payloads are briefly described, Table 1 gives a summary of 3 commonly used payloads and some other information that is effective for the suitable payload selection. It is worth mentioning that if needed, using multi-sensors is possible if they are compatible with the UAV platform. Also, other than these three common payloads, there are other payloads such as Sound Navigation and Ranging (SONAR) sensors which can be used for surface mapping while flying UAVs and obstacle detection [32]. Moreover, magnetic sensors can be used to generate magnetic maps and defect maps for ferrous materials like steel girders [33]. One of the major challenges related to visual cameras is the rolling shutter issue. Usually, UAVs are equipped with low-cost rolling-shutter cameras. Unlike in global-shutter cameras, when the aircraft collects the data with a rolling-shutter camera, each row is exposed in turn and thus poses differently [36]. Consequently, moving roller-shutter cameras often produce more image distortion [37]. Also, global-shutter cameras are more expensive and may not be affordable for inspection which means that new methods are needed for the use of the rollingshutter cameras or image distortion correction methods should be used during the data processing phase.

Table 1:Specifics of different common payloads and their limitations [15,31,34,35].

the impact of hardware on the data quality, safety regulations and pilot capabilities are two important factors at all steps of data collection. The pilot’s ability and comfort level during flight operations have a significant impact on the quality of the data obtained [38]. Considering the above-mentioned parameters for hardware selection and the challenges of this process, some of the recent studies of data collection and hardware selection are represented in Table 2 considering the UAV and the payload that they have used [39-59]. These studies investigate the effect of hardware selection on infrastructure inspection or the effect of the hardware that they used for inspection case studies which can be beneficial for future research in this area. Also in Figure 2, the general effective considerations for UAV and payload selection are shown. Some of these parameters are common between UAV and payload selection. In addition, Figure 3 represents the abovementioned challenges and limitations for hardware selection. Regarding payload selection, camera calibration is another area that has been studied in recent years. Camera calibration is necessary for UAV-based inspection due to the use of nonmetric, lightweight cameras that are not intended for photogrammetric accuracy [60]. Camera calibration is an essential step to extract metric data from 2D photos in 3D computer vision for correction of the image distortion [61]. In aerial images, pre-calibration or onthe- job calibration is frequently used to handle camera parameters, such as intrinsic parameters and lens distortion coefficients.

Table 2:Research related to hardware selection and data collection.

Also, generally, there are two methods of camera calibration: reference object-based calibration and self-calibration. Reference object-based calibration can be performed using 2D calibration benchmarks (such as checkboard patterns) or 3D physical calibration fields where coded markers are distributed in three dimensions with known coordinates [62]. 3D calibration is a highly accurate method, but costly and unsuitable for frequent recalibrations. On the other hand, 2D calibration is low-cost and it has been indicated that this method can achieve accurate results close to the 3D method [63]. For self-calibration, special calibration benchmarks are not required, and it depends on the structural information detected in images. Because of the flexibility and efficiency of this method, it has become a research area in recent years [6]. The goal of camera calibration is to establish the relationship between the 3D world coordinates of the object and their corresponding 2D image coordinates, forming the projection matrix. In Table 3, some of the studies that are related to camera calibration for RGB, thermal, and LiDAR cameras, especially for UAV applications are shown. After considering the inspection purpose, UAV and payload selection, and camera calibration, the next step for the preflight phase is to plan for the flight which is described and discussed in the next section.

Table 3:Research related to UAV camera calibration.

A good flight path is a crucial need for the use of UAS for inspection operations. Only if the mission is carefully planned to accomplish every inspection target can its full potential be used [75]. Path planning is challenging for UAV-based infrastructure inspection to find the optimal or near-optimal path. The flight path is a set of camera positions in which the images will be captured from these positions. Camera positions consist of the horizontal and vertical distance from the object, and the angle of the camera. The purpose of the flight path computation is to specify a collection of camera positions and viewing angles that are arranged in such a way that the structure being inspected is entirely covered by overlapping images at the specified resolution. The control of the UAS will then use those viewpoints as waypoints to automatically capture images [75]. In the process of selecting camera positions, one of the key considerations is the Ground Sampling Distance (GSD). The Ground Sampling Distance refers to the distance between two consecutive pixel centers measured on the ground. It plays a crucial role in determining the spatial resolution of the image and the level of visible details. A larger GSD value corresponds to lower spatial resolution, resulting in fewer visible details in the captured images.

For the selection of appropriate GSD, Debus et al. propose three distinct levels of interest for conducting the inspection, [75]. 2.0 mm/pixels, 1.0 mm/pixels, and 0.1 mm/pixels are defined as level 1(for rough geometry), level 2 (for detailed geometry), and level 3 (for crack detection) of interest respectively, which provides valuable guidance for tailoring the GSD and camera positions to effectively meet the inspection objectives. Also, according to the Specifications for the National Bridge Inventory 2022 (SNBI), the following quantitative standards are considered to categorize the cracks by their width and GSD can be selected based on the required crack detection:

a) Insignificant - crack width less than 0.004 inches (prestressed) or 0.012 inches (reinforced), or medium width cracks that have been sealed.

b) Medium - crack width ranging from 0.004 – 0.009 inches (prestressed) or 0.012 to 0.05 inches (reinforced).

c) Wide - crack width wider than 0.009 inches (prestressed) or 0.05 inches (reinforced).

To ensure comprehensive coverage and accurate data collection, it is generally recommended to have at least a 50% overlap of images between consecutive camera positions, as suggested by various studies [9]. This overlapping ensures that critical details are captured redundantly, minimizing the risk of missing essential information. For Infrastructure inspection using UAVs, there are two major methods for flight path planning: Manual Flight and Autonomous Flight. Also, an inspector may prefer to inspect with a combination of these methods. The principles for both methods are almost similar but it’s worth noting that in the past years, some research has explored autonomous flight path planning [27,34,76,77]. Obstacle avoidance especially in urban environments, multi-UAV path planning, and coverage path planning in 3D space are some of the challenges related to auto flight and some studies focused on addressing these issues [78-80]. Online or real-time path generation is another challenging area where the path is generated dynamically based on the data collected from the sensors and it is hard to ensure a sub-optimal path for this type of path generation. Some real-time path planners generate and modify their path while the dynamic obstacles are detected [81]. Another challenging area for path planning is to optimize flight path planning, especially for coverage problems and path planning for 3D flights because finding the shortest path in a 3D environment is NP-hard [82]. This task poses a challenging optimization problem, as it involves minimizing the objective cost function considering all relevant parameters such as obstacle avoidance. Most of the studies define path length as the objective function [83]. However, some studies define flight time or energy consumption [84], or flight altitude [85] as the objective cost function. Different optimization methods such as graph theoretical methods and meta-heuristics can be used for this purpose. Meta-heuristic algorithms excel at handling complex optimization problems by simulating natural phenomena and efficiently exploring large search spaces but they can be computationally costly [86].

Some studies applied these algorithms for flight path optimization. Qu et al. presented two methods for UAV path planning during the multi-thread circumstance. The first method is based on genetic algorithms and is suitable for offline path planning. The other method is fit for computing in real-time on UAV and is based on A-star heuristically search [87]. In another research, Phung et al. studied the inspection path planning using the traveling salesman problem and then optimization was applied by using discrete particle swarm optimization [88]. Table 4 shows the studies related to flight path planning, especially for flight path optimization which is an area that gained more attention during the last years as illustrated in the table below. Also, Figure 4 shows the above-mentioned challenges for flight path planning [89-114].

Table 4:Flight path planning-related studies.

With advancements and the expanding scope of research on Unmanned Aerial Vehicles (UAVs), addressing the issues and challenges associated with this platform holds considerable benefits, especially in fields like infrastructure inspection. The effectiveness of UAV-based inspections heavily relies on data quality, it becomes crucial to overcome challenges and limitations in pre-flight planning for optimal data collection. This review study delves into the pivotal phase of UAV-based infrastructure inspection, discussing the progress and remaining challenges related to the selection of hardware (UAV and payload), camera calibration, and flight path planning for UAVs. The increasing interest among researchers in achieving these advancements and addressing challenges is thoroughly explored. Furthermore, each challenge highlighted in this study presents an opportunity for future research endeavors aimed at optimizing UAV-based infrastructure inspection by resolving these issues.

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