Aerial Camera Research Articles

Two-mirror aerial mapping camera design with a tilted-aplanatic secondary mirror for image motion compensation.

The optical compensation method is a promising method to compensate the image motion by driving the optical element of the system. However, the tilt motion of the optical element could induce the degradation of the image quality. Based on the Nodal Aberration Theory (NAT), this paper proposes a novel optical image motion compensation design with a secondary tilt-aplanatic two-mirror optical system (STATOS), which achieves the insensitivity on the image quality of the optical system. First, the properties of coma-free pivot point depending on the location of stop at primary mirror or secondary mirror have been analyzed respectively. Then the calculating method about construction parameters in terms of STATOS is developed. Further, the maximum tilt angle of secondary mirror could be solved using NAT under ordered specific restriction. Finally, a prototype is designed with SM-tilting result of MTF value maintain above 0.32 at the cut-off frequency of detector, where the principal distance could remain stable and the changed distortion is better than 0.03%. A test flight has been carried out to prove the desired results and feasibility of STATOS used in an aerial mapping camera.

Targeting technology based on aerial monocular camera

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Sharpness Detection Method for Aerial Camera Images Based on Digital Elevation Model

Sharpness Detection Method for Aerial Camera Images Based on Digital Elevation Model

İNSANSIZ HAVA ARAÇLARI İÇİN U-NET TABANLI ARAÇ TESPİT YÖNTEMİ

With the developments in computer hardware technology, studies in the fields of computer vision and artificial intelligence has accelerated. However, the number of areas where autonomous systems are used has also increased. Among these areas are unmanned aerial vehicles, which are one of the most important parameters of today's military technology. In this study, which includes two different scenarios, we aimed to improve the vision capabilities of unmanned aerial vehicles based on artificial intelligence. Within the scope of Scenario-1, the U-Net model suitable for binary semantic segmentation method was trained with the help of images taken by unmanned aerial vehicle camera. Within the scope of Scenario-2, which is designed for moving or stationary vehicle detection, the U-Net model is trained in accordance with multi-class semantic segmentation method. In all these training processes, a publicly available dataset was used. The model trained for Scenario-1 reached mean Intersection over Union (mIoU) value of 84.3%, while the model trained for Scenario-2 reached 79.7% mIoU. In this study, approaches were shared about the use of high-resolution images in model training and testing stages. Applying such studies in the field can help improve precision and reliability in arms industry.

Decoupling Control of an Aviation Remote Sensing Stabilization Platform Based on a Cerebellar Model Articulation Controller

To obtain high-resolution and high-precision images, the aviation remote sensing stabilization platform (ARSSP) is used, which enables the isolation of unpredictable aerial camera movements during aerial photography. However, because of the interaxle coupling interference and other nonlinear interferences in the ARSSP system, the imaging quality of the aerial camera is adversely affected. Therefore, we derived the dynamic model of moment coupling between shafts to illustrate the problem. On the basis of the former proportion integration differentiation (PID) controller based on cerebellar neural network, a nonlinear cross feedback decoupling scheme is adopted to reduce the adverse effects of these interferences. The cerebellar model articulation controller (CMAC) based on synovial membrane control (SMC) is used to reduce the nonlinear interferences caused by the new cross-decoupling module. To verify the effectiveness of the scheme, simulation, indoor and outdoor experiments were conducted. The results showed that the SMC-CMAC significantly reduced the interaxle coupling effect and effectively suppressed the nonlinear interference, resulting in a good tracking performance of the ARSSP system.

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras’ self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.

Estimation of Human Body Height Using Consumer-Level UAVs

Consumer-level UAVs are often employed for surveillance, especially in urban areas. Within this context, human recognition via estimation of biometric traits, like body height, is of pivotal relevance. Previous studies confirmed that the pinhole model could be used for this purpose, but only if the accurate distance between the aerial camera and the target is known. Unfortunately, low positional accuracy of the drones and the difficulties of retrieving the coordinates of a moving target like a human may prevent reaching the required level of accuracy. This paper proposes a novel solution that may overcome this issue. It foresees calculating the relative altitude of the drone from the target by knowing only the ground distance between two points visible in the image. This relative altitude can be then used to calculate the target-to-camera distance without using the coordinates of the drone or the target. The procedure was verified with real data collected with a quadcopter, first considering a controlled environment with a wooden pole of known height and then a person in a more realistic scenario. The verification confirmed that a high level of accuracy can be reached, even with regular market drones.

The role of yielding cameras in pedestrian-vehicle interactions at un-signalized crosswalks: An application of game theoretical model

ObjectivePedestrian–vehicle interaction has gained substantial interest in the field of travel behavior and safety. Although traffic rules stipulate the right of way at unsignalized crosswalks, the actual behavior of road users is usually based on game-theoretical solutions. Moreover, non-cooperative games between pedestrians and drivers can be more complicated in the presence of law enforcement equipment. This study aimed to investigate the impact of yielding cameras on road users’ joint behavior at unsignalized crosswalks using a game-theoretical model.Methods and analysisA field survey was conducted using aerial and roadside cameras. In the game-theoretical model, road users were assumed to behave in accordance with the quantal response equilibrium. Additionally, the presence of a yielding camera was introduced into the payoff functions. Influencing factors, including demographics, road users’ behavioral characteristics, and environment, were incorporated into road users’ utilities. The beliefs of road users regarding other road users’ actions were also considered in the model. Furthermore, a questionnaire survey was conducted to investigate road users’ knowledge and awareness of yielding cameras and their right of way.ResultsThe results indicate that the yielding camera significantly affected driver behavior but had minimal effect on pedestrians crossing decisions because fewer pedestrians were aware of the yielding camera while crossing. Moreover, the camera increased the probability of confusion in the game between pedestrians and drivers (drivers chose to yield, whereas pedestrians chose to stay), thus adversely affecting traffic efficiency.Novelty and improvementA novel game-theoretical model was established to evaluate road users’ joint behaviors at unsignalized crosswalks. Several practical suggestions have been provided to improve the effectiveness of yielding cameras and traffic efficiency.

Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos

Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h’ high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately.

Simulating a Hybrid Acquisition System for UAV Platforms

Currently, there is a rapid trend in the production of airborne sensors consisting of multi-view cameras or hybrid sensors, i.e., a LiDAR scanner coupled with one or multiple cameras to enrich the data acquisition in terms of colors, texture, completeness of coverage, accuracy, etc. However, the current UAV hybrid systems are mainly equipped with a single camera that will not be sufficient to view the facades of buildings or other complex objects without having double flight paths with a defined oblique angle. This entails extensive flight planning, acquisition duration, extra costs, and data handling. In this paper, a multi-view camera system which is similar to the conventional Maltese cross configurations used in the standard aerial oblique camera systems is simulated. This proposed camera system is integrated with a multi-beam LiDAR to build an efficient UAV hybrid system. To design the low-cost UAV hybrid system, two types of cameras are investigated and proposed, namely the MAPIR Survey and the SenseFly SODA, integrated with a multi-beam digital Ouster OS1-32 LiDAR sensor. Two simulated UAV flight experiments are created with a dedicated methodology and processed with photogrammetric methods. The results show that with a flight speed of 5 m/s and an image overlap of 80/80, an average density of up to 1500 pts/m2 can be achieved with adequate facade coverage in one-pass flight strips.

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessment, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages. In this study, we utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost, to compensate for the deficiency of forestry inventory using UAS-based LiDAR that requires terrain-following flight for high-resolution data acquisition. Combined with the sparse LiDAR points acquired by using a high-altitude and high-speed UAS for terrain extraction, dense normalized DAP point clouds can be obtained to produce an accurate and high-resolution canopy height model (CHM). Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency. It is proved that sparse LiDAR point cloud could serve as important sources for terrain estimation. The accuracy of the AGB estimates could be improved by 9% and 5% in terms of the RMSE and R2, respectively, by adding spectral metrics to point cloud structural metrics. Compared with results obtained using only spectral metrics, the results obtained using the combination of spectral and point cloud metrics were better by 37% in terms of the RMSE and 55% in R2. Our study supports the development of predictive models for large-scale wall-to-wall AGB mapping by leveraging the complementarity between DAP and LiDAR measurements. This work also reveals the potential of utilizing a UAS-based digital camera and LiDAR synergistically in a plateau mountainous forest area. In this future, we will further investigate different establishing strategies for combining different sensors in a synergistic system.

Early Wildfire Detection Technologies in Practice—A Review

As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected and precisely geolocated while still in their nascent phases. Furthermore, automated approaches without human involvement are comparatively more efficient, accurate and capable of monitoring extremely remote and vast areas. With this specific intention, many research groups have proposed numerous approaches in the last several years, which can be grouped broadly into these four distinct categories: sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review paper discusses notable advancements and trends in these categories, with subsequent shortcomings and challenges. We also describe a technical overview of common prototypes and several analysis models used to diagnose a fire from the raw input data. By writing this paper, we hoped to create a synopsis of the current state of technology in this emergent research area and provide a reference for further developments to other interested researchers.

Thermal-optical characteristics analysis of an aerial camera optical system.

Ambient temperature is one of the important factors affecting the imaging quality of the optical system. Therefore, it is necessary to analyze the thermal-optical characteristics of the optical system when studying the imaging quality of the optical system. Taking the self-made aerial camera optical system as an example, this paper reports the use of the finite element software ANSYS to analyze the thermal stress of the aerial camera optical system, the use of the homogeneous coordinate transformation method to remove the rigid body displacement caused by the mirror surface, and the performance of a Zernike polynomial simulation on the processed surface data. Together, the Zernike coefficients obtained after the fitting are substituted into the ZEMAX optical software to express the surface shape obtained after deformation to analyze the changes in optical imaging quality under thermal environmental conditions. The results of the experiment show that when the working environmental temperature is 50°C, the (MTF) decreases from 0.371 to 0.315 and, when the working environmental temperature is -40∘C, the MTF decreases from 0.371 to 0.285. The comparison results of the experiment and simulation show that the environmental temperature is heating up or, under the condition of cooling, the imaging quality of the aerial camera will be reduced. The thermal-optical analysis method can be used to simulate the actual use conditions of the aerial camera optical system and predict the imaging quality change trend of the optical system. The method of thermo-optical analysis has important guiding significance for the design of an optical-mechanical system.

Real-time and high precision feature matching between blur aerial images.

When aerial cameras get aerial remote sensing images, the defocus will occur because of reasons such as air pressure, temperature and ground elevation changes, resulting in different image sharpness of continual aerial remote sensing images. Nowadays, the rapidly developing feature matching algorithm will rapidly reduce the registration rate between images with different image sharpness. Therefore, in order to enable aerial cameras to get image sharpness parameters according to the locations of aerial image feature points with inconsistent sharpness, this paper proposes a feature matching algorithm between aerial images with different sharpness by using DEM data and multiple constraints. In this paper, the feature matching range is extended according to the modified aerial imaging model and the nonlinear soft margin support vector machine. Then the relative moving speed and its variation of the feature points in the image are obtained by using the extended L-k optical flow, and finally the epipolar geometric constraint is introduced. To locate the feature points is obtained under multiple constraints, there is no need to calculate the feature point descriptors, and some mismatched point pairs are corrected, which improves the matching efficiency and precision. The experimental results show the feature matching precision of this algorithm is more than 90%, and the running time and matching precision can meet various application needs of aerial cameras.

Surveillance Technologies and Constitutional Law.

This review focuses on government use of technology to observe, collect, or record potential criminal activity in real-time, as contrasted with "transaction surveillance" that involves government efforts to access already-existing reviews (Brayne 2018, Ridgeway 2018). Even so limited, surveillance technologies come in many guises, including closed-circuit television, automated license plate and facial readers, aerial cameras, and GPS tracking. Also classifiable as surveillance technology are devices such as thermal and electromagnetic imagers that can "see" through walls and clothing. Finally, surveillance includes wiretapping and other forms of communication interception. The following discussion briefly examines the limited evidence we have about the prevalence and effectiveness of these technologies and then describes the law governing surveillance, focusing principally on constitutional doctrine, and how it might-and might not-limit use of these technologies in the future.

Design balance autonomous quadcopter for aerial photography with kalman

The purpose of this research is to develop a cheap, accurate and autonomous quadcopter. This quadcopter contains microcontroller, gyroscope, accelerometer, barometer, electronic governor and brushless motor. At present, the noise removal filter of the quadcopter sold on the market is a complementary filter, and its disadvantage is that it will produce distortion when the attitude changes at a large angle. Therefore, in order to improve the shortcomings of the complementary filter in the quadcopter, we use the Kalman filter to replace complementary filters to achieve goal. At the same time, through the bluetooth remote connection, the flight situation can be immediately known, and the remote unmanned and stable flight of the quadcopter aerial camera can be achieved, so that the disaster areas or places that are difficult for exploration personnel to quickly obtain stable and clear images for convenient arrangement of rescue operations.

Mutation detection dual correlation filter with an object-awareness module for real-time target tracking.

Discriminative correlation filter (DCF) based methods have recently been widely used for visual tracking tasks. The adaptive spatiotemporal-regulation based tracker (AutoTrack) can only partially solve some limitations of the DCF framework including filter degradation and the boundary effect, but its application scenarios need to be broadened, and performance improvements are also required. To further surmount these difficulties, this paper provides an object-awareness-module based mutation detection dual correlation filter (MDDCF-OAM). The main innovation points of this work are: (1) an object-mask based context enhancer is proposed to formulate a more robust appearance model; (2) a dual filter training-learning structure is adopted to allow the dual filters to restrict each other and suppress the filter degradation effect; (3) a Gaussian label map is updated with the refined joint response map to detect and attenuate the response mutation effects. Exhaustive experiments have been conducted to test the efficiency of the suggested MDDCF-OAM on four benchmarks, namely, OTB2015, UAV123, TC128, and VOT2019. The results indicate that: (1) the introduced MDDCF-OAM surpasses nine state-of-the-art trackers; (2) the MDDCF-OAM has a real-time speed of 32 frames per second, which is sufficient for target tracking tasks in numerous scenarios, especially unmanned aerial vehicles and camera tracking.

Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique.

Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%.

A Quadrifocal Tensor SFM Photogrammetry Positioning and Calibration Technique for HOFS Aerial Sensors

Nowadays, the integration between photogrammetry and structure from motion (SFM) has become much closer, and many attempts have been made to combine the two approaches to realize the positioning, calibration, and 3D reconstruction of a large number of images. For the positioning and calibration of high oblique frame sweep (HOFS) aerial cameras, a quadrifocal tensor SFM photogrammetry technique is proposed to resolve the positioning and calibration task of such cameras. It adopts the quadrifocal tensor idea into the OpenMVG SFM pipeline to solve the complexity problem caused by the small single-viewing imaging area and the high image overlapping ratio. It also integrates the photogrammetry iteration idea into the OpenMVG SFM pipeline to enhance the positioning and calibration accuracy, which includes a coarse to fine three-stage Bundle Adjustment (BA) processing approach. In this paper, the overall workflow of the proposed technique was first introduced in detail, from feature extraction and image matching, relative rotation and translation estimation, global rotation and translation estimation, and the quadrifocal tensor model construction to the three-stage BA process and calibration. Then, experiments were carried out in the Zhengzhou area, implementing four types of adjustment methods. The results suggest that the proposed quadrifocal tensor SFM photogrammetry is suitable for large tilt frame sweep camera positioning and calibration without prior information on detailed camera intrinsic parameters and structure. The modifications made to the OpenMVG SFM pipeline enhanced the precision of image positioning and calibration and provided the precision level of professional photogrammetry software.

ASSESSMENT OF THE ACCURACY OF PHOTOPLAN

When using analog aerial cameras, photomaps were assembled from transformed images. If the elevation of the points of the terrain made it possible to obtain acceptable distortions for the relief, then they were transformed into one zone. Otherwise, the transformation was performed by zones, allowing displacements in each zone of no more than 0.4 mm. In neighboring zones, the displacements at the zone boundary had opposite directions, and the discrepancy between the contours could reach 0.8 mm. Its average value was taken as the final position of the contour, which compensated for position errors in neighboring zones.
 
 When creating digital photomaps and orthophotomaps, digital elevation models (DEM) are used to account for terrain distortions. Digital transform technology does not use transform zones and therefore will not compensate for errors as it would with zone transforms. The quality of the orthomosaic will depend on the accuracy of the DEM. A digital model of a relief or surface has a high density and makes it possible to take into account distortions for the relief much more accurately than when transforming by zones.
 
 The paper analyzes the requirements of the current regulatory documents when creating digital photomaps and orthophotomaps, provides relevant conclusions and recommendations.