Camera Point Research Articles

Evaluative Performance Program E-Tilang di Simpang Raya Kota Surabaya

ETLE (Electronic Traffic Law Enforcement) is a set of electronic devices that use Automatic License Plate Recognition (ANPR) technology in the form of cameras that automatically detect motorized vehicles and cars with traffic violations E-TLE system, which is part of law enforcement efforts, this system aims to reduce problems caused by traffic violations, such as congestion and accidents, and ensure order on the highway. The e-Tilang system was pioneered by the East Java Regional Police, especially the Surabaya Police, which has this innovation as a crucial role in improving safety and regularity of electronically based traffic, the implementation of ETLE tickets in Surabaya City has increased in 2022 and 2023 compared to 2021. Which in 2021 the ETLE program was intensified due to the Covid-19 pandemic in order to increase the efficiency of technology-based services. The ETLE camera system uses CCTV cameras that are monitored directly by Police Officers with a total of 22 monitoring camera points in several parts of Surabaya City. where each officer oversees 4 areas with 8 CCTVs on the monitoring screen. Although it has had a positive impact on the implementation of ticketing, there are many things that should be evaluated from the process of running E-TLE at Surabaya Police Station. Mainly in the process of implementing and providing maximum fines, as well as the limited number of employees and time in ETLE supervision. So that the Process on Performance and the Facilities used should be evaluated in the running of the ETLE system in Surabaya City. The method used in this research is descriptive qualitative. The focus of this research is to find out the results of the Evaluation of the E-Ticket Policy Program (E-TLE) at the Surabaya City Road Intersection The theory used in the research is Gupta & Jana's Evaluative performance theory. There are 4 indicators from this theory, namely total costs and fines, quality of planning and control, quality of decision making and system characteristics. The data collection technique in this study is by conducting interviews, observation and documentation. In this research, from these 4 indicators, some are quite appropriate, but there are still many shortcomings that should be evaluated from the system and the quality of performance of the Surabaya police litas unit. The performance evaluation of the E-Tilang program in Surabaya shows success in managing total costs and fines, the quality of planning and control, and the quality of decision making. This program includes the use of 40 CCTV camera units and fiber optic networks that support the effectiveness of law enforcement, as well as the management of fair fines. Careful planning and better coordination after the evaluation ensured efficiency in recording violations using gadgets and the E-Tilang application.

A study on dual quaternion based cooperative relative navigation of multiple UAVs with monocular vision-inertial integration

This paper addresses a cooperative relative navigation problem for multiple aerial agents, relying on visual tracking information between vehicles. The research aims to investigate a sensor fusion architecture and algorithm that leverages partially available absolute navigation knowledge while exploiting collaborative visual interaction between vehicles in mission flight areas, where satellite navigation-denied regions are irregularly located. To achieve this, the paper introduces a new approach to defining the relative poses of cameras and develops a corresponding process to secure the relative pose information. This contrasts with previous research, which simply linearized the relative pose information of aircraft cameras into navigation states defined in an absolute coordinate system. Specifically, the target pose in relative navigation is defined, and the pose of the camera and feature points are directly derived using dual quaternion representation, which compactly represents both translation and rotation. Furthermore, a mathematical model for the relative pose of the camera is derived through the dual quaternion framework, enabling an explicit pose formulation of relative navigation. The study investigates navigation performance in typical mission flight scenarios using an in-house high-fidelity simulator and quantitatively highlights the contributions of the proposed scheme by comparing the navigation error performance. Consequently, the proposed method demonstrates to have navigation accuracy in decimeter level even in GNSS-denied environments and an improved 3D Root Mean Square (RMS) error by 30% smaller than the conventional absolute navigation framework.

Laser-based drone vision disruption with a real-time tracking system for privacy preservation

The capabilities of drones are increasing every day, as is the ease with which civilians can buy and fly them. Most drones are equipped with a camera that is used by a point-of-view operator and, at the same time, can be used for image capture. The use of drones creates a threat to privacy whereby anyone who can fly a drone can take pictures without permission. This study aims to create a 2-axis tracker system that can recognize a drone and locate the position of the drone camera so that a laser beam can track and dazzle the drone camera. The depth-sensing camera is used to localize the part of the target corresponding to the drone’s camera and is created using the YOLOv5 algorithm as a deep-learning detector model. The drone’s camera range and position are challenging to detect due to its small size. Our adaptive detection method combines drone detection and drone camera detection. The depth-sensing camera provides input in the form of a three-coordinate axis from the target. If only the drone is detected, a predictive algorithm can determine the camera’s position for illumination with the laser. Alternatively, if the drone camera is detected, the laser can follow the target’s movement more quickly. In this study, a green (520 nm) laser module with adjustable power is used to investigate factors that affect the dazzling range. The computer vision detection algorithm can detect and localize the position of the drone camera up to 500 cm with a confidence level of more than 65%. If the target is in the center of the field of view, the accuracy of the target position can reach 98%. The tracker can follow the drone’s movement from 2 m/s to 4 m/s with a maximum error of 1.9 cm from the center point of the drone camera for close range. For long range, the maximum error is 6.2 cm. A laser power of 23.5 mW at 500 cm distance is found to be sufficient to dazzle and track drone cameras.

Occurrence patterns of sympatric forest wallabies: assessing the influence of structural habitat attributes on the coexistence of Thylogale thetis and T.stigmatica.

We studied the occurrence of two sympatric wallabies, the red-necked pademelon (Thylogale thetis) and the red-legged pademelon (T. stigmatica) in northeastern New South Wales, Australia in relation to structural habitat attributes. At our study site, both species inhabit closed forest environments and have overlapping distributions, but T. thetis leaves the forest at night to graze adjacent grassy forest edges whereas T. stigmatica remains within the forest and browses forest vegetation. The objectives of the study were to investigate how structural attributes of two forest types, wet sclerophyll forest and rainforest, relate to the fine-scale occurrence of these two wallaby species within the forested environment. We gathered occurrence data from 48 camera trap stations divided equally between rainforest and wet sclerophyll forest. At each camera point, we also measured a range of structural habitat attributes to determine habitat affiliations for the two Thylogale species. Principal component analyses were used to describe major trends in habitat, and generalised linear models were used to describe the efficacy of the variables in predicting habitat occurrence of each species. The number of occurrences of Thylogale thetis was significantly greater than occurrences of T. stigmatica, which was driven by significantly greater occurrences of T. thetis in wet sclerophyll forest. There was both spatial and temporal partitioning between the two species; there was a significant difference in the occurrences of the two species at individual cameras and T. stigmatica had a different activity schedule than T. thetis in wet sclerophyll forest, where the latter reached its greatest rate of occurrence. At a finer (camera station) scale, occurrences of T. thetis increased with proximity to roads and grassy edges and at sites that were less rocky and less steep. T. stigmatica occurrence increased in the presence of rainforest elements like vines, palms and ferns, more ground-level cover and tree-fall gaps and at sites with fewer emergent eucalypts. Our findings have implications for managing these pademelons and their habitats. T. thetis is a common species that was encountered more often than T. stigmatica, and it responded positively to human disturbance like roadsides and grassy edges, presumably because these areas provided good grazing opportunities. By comparison, T. stigmatica is a threatened species, and it responded to natural disturbance like tree-fall gaps where lateral cover was greater, and where rainforest food plants may be more abundant. Our results suggest, therefore, that conservation of the threatened T. stigmatica requires the preservation of intact rainforest.

Korean Cattle 3D Reconstruction from Multi-View 3D-Camera System in Real Environment.

The rapid evolution of 3D technology in recent years has brought about significant change in the field of agriculture, including precision livestock management. From 3D geometry information, the weight and characteristics of body parts of Korean cattle can be analyzed to improve cow growth. In this paper, a system of cameras is built to synchronously capture 3D data and then reconstruct a 3D mesh representation. In general, to reconstruct non-rigid objects, a system of cameras is synchronized and calibrated, and then the data of each camera are transformed to global coordinates. However, when reconstructing cattle in a real environment, difficulties including fences and the vibration of cameras can lead to the failure of the process of reconstruction. A new scheme is proposed that automatically removes environmental fences and noise. An optimization method is proposed that interweaves camera pose updates, and the distances between the camera pose and the initial camera position are added as part of the objective function. The difference between the camera's point clouds to the mesh output is reduced from 7.5 mm to 5.5 mm. The experimental results showed that our scheme can automatically generate a high-quality mesh in a real environment. This scheme provides data that can be used for other research on Korean cattle.

YES-SLAM: YOLOv7-enhanced-semantic visual SLAM for mobile robots in dynamic scenes

In dynamic scenes, moving objects will cause a significant error accumulation in robot’s pose estimation, and might even lead to tracking loss. In view of these problems, this paper proposes a semantic visual simultaneous localization and mapping algorithm based on YOLOv7. First, a light-weight network YOLOv7 is employed to acquire the semantic information of different objects in the scene, and flood filling and edge-enhanced techniques are combined to accurately and quickly separate the dynamic feature points from the extracted feature point set. In this way, the obtained static feature points with high-confidence are used to achieve the accurate estimation of robot’s pose. Then, according to the semantic information of YOLOv7, the motion magnitude of the robot, and the number of dynamic feature points in camera’s field-of-view, a high-performance keyframe selection strategy is constructed. On this basis, a robust loop closure detection method is developed by introducing the semantic information into the bag-of-words model, and global bundle adjustment optimization is performed on all keyframes and map points to obtain a global consistent pose graph. Finally, YOLOv7 is further utilized to carry out semantic segmentation on the keyframes, remove the dynamic objects in its semantic mask, and combine the point cloud pre-processing and octree map to build a 3D navigation semantic map. A series of simulations on TUM dataset and a case study in real scene clearly demonstrated the performance superiority of the proposed algorithms.

HYBRID ADJUSTMENT OF UAS-BASED LiDAR AND IMAGE DATA

Abstract. Several advancements are going with Unmanned Aerial Systems (UAS) with the addition of multiple sensors and simultaneous data acquisition to obtain detailed geo-data for various applications. However, simultaneous data acquisition with multiple sensors, namely camera, and LiDAR, will also result in possible discrepancies associated with them, and they need to be solved to use a reliable and accurate final product. Several errors can be associated with both camera and LiDAR datasets due to the different characteristics of the sensors and terrain conditions. This research paper aimed to minimize the errors between LiDAR and the image datasets simultaneously acquired with an Unmanned Aerial System (UAS) by implementing a hybrid adjustment approach with a criterion for the roughness and threshold angle between surface normals. The initial trajectory of the UAS, raw LiDAR measurements, and image observations were the inputs used for the hybrid adjustment. The hybrid adjustment workflow minimizes the discrepancies with a least-squares-based simultaneous adjustment for both LiDAR and image datasets. For the hybrid adjustment process, three types of correspondences were established, namely: between image pairs, overlapping LiDAR strips, and between Image tie points and LiDAR strips. For quality control, mean Cloud-to-Cloud distances (C2C) were compared between both LiDAR and camera point clouds before and after hybrid adjustment. The surface-level analysis of the results was also carried out to analyze the errors before and after hybrid adjustment at a surface level for different types of surfaces. The results showed that the alignment between the point clouds has significantly improved from the range of meters to a centimeter-level after implementing the hybrid adjustment process. The proposed hybrid adjustment workflow can be used in mapping applications where a centimeter-level accuracy is requested.

Sign Language Recognition with Multimodal Sensors and Deep Learning Methods

Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues. In this research, we aim to improve accuracy through data fusion of 2-axis bending sensors and computer vision. We obtain the hand key point information of sign language movements captured by a monocular RGB camera and use key points to calculate hand joint angles. The system achieves higher recognition accuracy by fusing multimodal data of the skeleton, joint angles, and finger curvature. In order to effectively fuse data, we spliced multimodal data and used CNN-BiLSTM to extract effective features for sign language recognition. CNN is a method that can learn spatial information, and BiLSTM can learn time series data. We built a data collection system with bending sensor data gloves and cameras. A dataset was collected that contains 32 Japanese sign language movements of seven people, including 27 static movements and 5 dynamic movements. Each movement is repeated 10 times, totaling about 112 min. In particular, we obtained data containing occlusions. Experimental results show that our system can fuse multimodal information and perform better than using only skeletal information, with the accuracy increasing from 68.34% to 84.13%.

Frequent prescribed burns reduce mammalian species richness and occurrence in longleaf pine sandhills

Prescribed fire is a critical forest management tool, the frequency and size of which can alter the composition of wildlife communities. In the longleaf pine ecosystem of the southeastern United States, frequent prescribed fire (1–3 year fire interval) is used to replicate natural processes that prevent woody encroachment and transition to alternate states. However, we have little understanding of how different scales and frequencies of fire influence medium and large mammals. To address this knowledge gap, we assessed the homogenizing influence of prescribed fire on mammal community composition at different fire frequencies and scales. We set 110 camera trap grids containing 990 individual camera points across conservation lands in north Florida and the Florida panhandle. We used a Bayesian multi-species occupancy modeling approach to assess the relationship between fire frequency and the occurrence of 11 mammal species across three spatial scales (0.2 ha, 12.57 ha, and 176.71 ha). Species richness was negatively associated with increased burn frequency at all scales and community occurrence was negatively associated with increased burn frequency at the two largest scales. Non-native nine-banded armadillos were negatively associated with increased burn frequency at all scales and non-native feral hogs were negatively associated with increased burn frequency at the two largest scales. Raccoons and opossums, both known nest predators, were negatively associated with increased burn frequency at the two largest scales. Our results indicate that prescribed fire applied at 1–3 year intervals could be used to reduce the occurrence of most non-native mammals and several prolific nest predators in longleaf pine sandhills, particularly at larger scales. However, these perceived benefits must be weighed against the concurrent loss of the ecosystem services and functions native generalist species provide.

A stable vision-based pose measurement approach by fusing the plane constraints formed by the control points and optical center

Due to the existence of noise, the non-iterative pose measurement approaches tend to be unstable when there are no redundant 3D control points. The pose measurement is easily affected by the measurement factors, i.e., the shape composed of the control points, the size of the shape composed of the control points, and the distance between the camera and control points. In order to enhance the anti-noise ability, thereby reducing the influence of the measurement factors, reducing the pose measurement error, and improving the pose measurement stability, a non-iterative pose measurement approach is proposed based on fusing the plane constraints formed by the control points and optical center. In addition to the correspondence between each control point and the corresponding image point, the length between any two control points is considered. The length constraint is converted to the plane constraint formed by these two control points and the optical center because the plane constraint can be fused in the non-iterative pose measurement model established by the correspondence between each control point and the corresponding image point. Experimental results show the advantages of our pose measurement approach.

Object SLAM With Robust Quadric Initialization and Mapping for Dynamic Outdoors

Object SLAM is a popular approach for autonomous driving and robotics, but accurate object perception in outdoor environments remains a challenge. State-of-the-art object SLAM algorithms rely on assumptions and are sensitive to observation noise, limiting their application in real-world scenarios. To address these challenges, we propose a novel object SLAM system that utilizes a quadric initialization algorithm based on constrained quadric optimization, which does not rely on planar assumptions and is robust to partial observations. Additionally, we introduce an automatic object data association algorithm capable of detecting motion states while associating objects across frames. To further enhance the accuracy of the quadric mapping, an extra thread is used to refine the ellipsoid parameters within a local sliding window composed of keyframes. Our system utilizes a joint optimization framework that optimizes camera poses, object landmarks, and point clouds in the local mapping thread for further global optimization while maintaining a consistent map. Experimental results on the real-world KITTI dataset show that the proposed system is more robust and significantly outperforms current state-of-the-art methods in quadric initialization and mapping in outdoor scenarios. Moreover, our system achieves real-time performance, making it suitable for practical applications.

Imaging model and vision measurement method considering refraction effect of flat glass windows in tunnel construction

Vision measurement has become an indispensable technology in tunnel construction. However, the harsh construction environment necessitates the provision of a certain thickness of flat glass windows to protect the vision system. This, in turn, leads to an obvious refraction effect, rendering the pinhole model and traditional vision measurement methods inadequate. As such, this paper offers a comprehensive solution to this problem. Firstly, the imaging model under the refraction of flat glass is shown based on the virtual camera and virtual space point, respectively. Secondly, a three-parameter calibration method for flat glass was proposed, which could determine the refractive index, unit normal vector, and light path normal distance. A practical dynamic interval constraint was introduced to address the failure of the epipolar constraint in refraction image pairs. Lastly, a new binocular disparity method is presented to avoid the impact of refraction distortion on the solution accuracy of three-dimensional coordinates. Effectiveness experiments are conducted, showing a refractive index relative error within ±1%, a miniaturized and efficient dynamic interval constraint, a maximum reconstruction error of 0.120 mm, and a maximum measurement error of only 0.093 mm. A series of stability experiments are also performed under different target attitudes, glass attitudes, working distances, and pollution conditions. The results demonstrate the robustness of the proposed solution. Although the scheme is developed for the vision system in harsh tunnel construction environments, it can serve as a reference for any vision measurement applications under glass protection windows.

A Point Cloud Classification Method and Its Applications Based on Multi-Head Self-Attention

<p>In the monitoring the safety status of hazardous chemical warehouses by three-dimensional re-construction of deep camera point clouds, there are classification difficulties such as large space, sparse distribution of point clouds in cargo images, and similar distribution in low dimensions. Based on the above problem, a point cloud recognition method based on multi-head attention mechanism is proposed. The algorithm first normalizes the distribution of the point cloud data set through the affine transformation algorithm to solve the problem of sparse distribution. Then, the high-dimensional feature map is obtained by fusing the data down-sampling and curve feature aggregation algorithms to solve the problem of low-dimensional distribution approximation. The feature map is then encoded using a multi-head self-attention encoder to obtain features under different heads, which are then merged into a feature map. Finally, a multi-layer fully connected neural network is used as the decoder to decode the feature map into the final object classification. Comparative experiments were performed on the ModelNet40 dataset and the self-built dataset of warehouse goods, and the results showed that the accuracy of this paper was improved by 0.5% to 7.8% compared with that of other classification algorithms.</p> <p> </p>

RepC-MVSNet: A Reparameterized Self-Supervised 3D Reconstruction Algorithm for Wheat 3D Reconstruction

The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization.

Avian species survey with citizen-science data in Janghang Wetland, Goyang, Republic of Korea.

Monitoring of avian populations in Janghang Wetland, Goyang, Republic of Korea (ROK) is based on citizen science (also called community-based monitoring). This monitoring data can be used to track avian density, population status and waterbird census at local, national and regional levels. The Ministry of Environment (MoE) ROK has surveyed since 1999, including Odusan Unification Tower to Ilsan Bride, which connects the cities of Gimpo and Goyang along the Han River estuary. However, it has not covered Janghang Wetland, which is located in the Han River estuary at the transboundary between the two Koreas. The Janghang Wetland is a protected wetland in the Demilitarized Zone (DMZ) between the two Koreas. In 2019, Janghang Wetland was designated as a Flyway Network Site by Goyang City and the East Asian-Australasian Flyway Partnership. This Network site is a voluntary collaboration and includes many internationally significant wetlands for waterbirds that still lack formal national protection. In addition, it was designated as a Ramsar site in 2021. The wetland currently supports wintering population of White-naped Crane (Grusvipio), species listed as vulnerable and Tundra Bean Goose (Ansercygnoides), spring-autumn migration population of Swan Goose (Ansercygnoid), species listed as vulnerable and a breeding population of Black-faced Spoonbill (Plataleaminor), species listed as endangered in summer. We provide data that the Janghang Wetland is a significant area for migration and breeding for waterbirds; and that Han River estuary is also internationally important for waterbirds during the migratory bird season. We observed 14 orders, 42 families and 132 species. The surveys also observed the critically-endangered Black-faced Spoonbill (Plataleaminor), Swan Goose (Ansercygnoides), White-naped Crane (Grusvipio), Whooper Swan (Cygnuscygnus) and Peregrine Falcon (Falcoperegrinus). We also observed the Black-faced Spoonbill, Great Egret, Little Egret, Great Cormorant, Eastern Spot-billed Duck, Pheasant and Brown-eared Bulbul at the sensor camera point and White-naped Crane, Hooded Crane, Bean Goose, White-fronted Goose, Snow Goose, Swan Goose, Great Cormorant and Eastern Spot-billed Duck at the closed-circuit television camera point from the camera-trap surveys. Based on the species recorded, the survey area is of clear importance for biodiversity conservation.

Structured light 3-D sensing for scenes with discontinuous reflectivity: error removal based on scene reconstruction and normalization.

Structured light-based 3-D sensing technique reconstructs the 3-D shape from the disparity given by pixel correspondence of two sensors. However, for scene surface containing discontinuous reflectivity (DR), the captured intensity deviates from its actual value caused by the non-ideal camera point spread function (PSF), thus generating 3-D measurement error. First, we construct the error model of fringe projection profilometry (FPP). From which, we conclude that the DR error of FPP is related to both the camera PSF and the scene reflectivity. The DR error of FPP is hard to be alleviated because of unknown scene reflectivity. Second, we introduce single-pixel imaging (SI) to reconstruct the scene reflectivity and normalize the scene with scene reflectivity "captured" by the projector. From the normalized scene reflectivity, pixel correspondence with error opposite to the original reflectivity is calculated for the DR error removal. Third, we propose an accurate 3-D reconstruction method under discontinuous reflectivity. In this method, pixel correspondence is first established by using FPP, and then refined by using SI with reflectivity normalization. Both the analysis and the measurement accuracy are verified under scenes with different reflectivity distributions in the experiments. As a result, the DR error is effectively alleviated while taking an acceptable measurement time.

Pendampingan Pembenahan Instalasi Sistem CCTV untuk Masjid Baitul Rohmah Pudak Payung Semarang

Apart from being a place of worship, the mosque has a function to improve community relations in the environment around the mosque in particular. In order to improve community relations, it is necessary to hold several activities involving the community under the auspices of the mosque. In order for community activities to take place, the mosque is equipped with several assets such as electronic devices, sound systems, air conditioning, infaq contacts/charity boxes and some other supporting furniture. So mosque assets need to be maintained in the security aspect. To support the security aspects of the mosque's environment, in this service there will be mentoring and training on the CCTV (Close Circuit Television) system. This service activity is carried out by technicians, mosque congregation and accompanied by a service team, including; needs assessment, field survey, network design and layout, preparation of budget plans and device requirements, device installation and device firmware settings. After being agreed by the team, this service installed 5 camera points to monitor 4 areas, parking area, yard, congregation area and priest area. From the results of this activity, that the installation of cameras at 5 points in accordance with the planned survey results and needs assessment can provide comprehensive monitoring of environmental conditions outside and inside the mosque, so that it can better guarantee security in the mosque and its surroundings.

Height Measurements of Pinus Brutia Ten. from Uav- 1" Cmos Digital Camera Point Clouds in Geverke - Duhok Governorate, Kurdistan Region of Iraq

Height Measurements of Pinus Brutia Ten. from Uav- 1" Cmos Digital Camera Point Clouds in Geverke - Duhok Governorate, Kurdistan Region of Iraq

Design and Implementation of a Detachable Monitoring Biomass Boiler

This paper studies a removable monitoring biomass boiler, including the boiler body. One side of the boiler body is connected with a blanking pipe, the lower end of the middle part of the blanking pipe is provided with a protective cover, the side of the protective cover close to the boiler body is provided with a transparent protective plate, the protective cover is provided with a fixed block, the fixed block is provided with a camera, the transparent protective plate is arranged between the camera and the boiler body, and the shooting direction of the camera points to the boiler body. In this boiler, the camera can capture the combustion of fuel in the boiler body. The fuel is input into the boiler body through the blanking channel, which does not affect the shooting of the camera. The dust and flue gas generated during the combustion of biomass boiler will flow out through the blanking channel, and less dust and flue gas will adhere to the transparent protective plate in front of the camera, which is convenient for the camera to capture the combustion of fuel in the boiler body.

UAV positioning algorithm based on ground reference

In this article, the algorithm of UAV positioning based on ground check points is considered. Differ from the most publications, in which UAV coordinates are determined by GPS or/and IMU, the proposed algorithm used results of image processing technology. Thank to image processing technique, the coordinates of ground check points in camera’s frame are determined, then UAV coordinates will also be determined. With the measured parameters such as UAV’s orientation and attitude, camera’s elevation and azimuth angle, position of UAV relative to ground check points is determined, then other UAV coordinates are also determined. The proposed algorithm is mathematically proved and verified by symulation.