Distant Objects Research Articles

A 6-DOF camera motion correction method using IMU sensors for photogrammetry and optical measurements

Environmental conditions such as wind and ground traffic introduce motion in camera measurement systems and affect measurement accuracy. Conventional camera motion correction methods track static reference points with one or multiple cameras, reducing applicability. This study proposes a novel 6-degree-of-freedom (DOF) camera motion correction method using only an inertial measurement unit (IMU) sensor. A Kalman filter is adopted as a data fusion method to estimate the camera orientation and translation using IMU data. Six pinhole camera models are built to evaluate and correct 6-DOF camera motions. The motion correction efficiency and robustness are tested for different object distances and focal lengths of optical lenses. The motion correction ratio is statistically analysed and reaches approximately 80%. The object distance has little effect on the motion correction ratio. The rotation-induced pixel movement is independent of the object distance. More than 90% of the pixel movement noise is caused by camera rotation. The translation-induced pixel movement is inversely correlated with the object distance.

Location and segmentation of important railway signs based on improved segmentation

Purpose With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection. Design/methodology/approach This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure. Findings This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs. Research limitations/implications The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate. Social implications The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security. Originality/value This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

L[formula omitted]OP-ART: A linear-time adaptive random testing algorithm for object-oriented programs

Object-oriented (OO) programming offers useful and desirable characteristics in the resulting code. These OO characteristics, however, demand an effective testing method. Adaptive random testing (ART) serves as an effective testing technique where test cases are distributed uniformly according to a proper metric for difference (distance) between test cases. Recently proposed object and method invocation sequence similarity (OMISS) distance metric was incorporated into a new ART algorithm (OMISS-ART) for testing OO software. Unfortunately, OMISS-ART’s high effectiveness in finding faults comes at significantly higher computational overheads. To alleviate the problem, we present in this paper a linear-time ART algorithm called L′OP-ART (Linear-time Object-oriented Program testing by ART) based on a lightweight OMISS metric. L′OP-ART designs specific data structures that store information about executed test cases, thus, the differences between candidates and executed test cases can be obtained with reduced overheads. Empirical studies show that L′OP-ART detects failures more effectively than random testing (RT), and has comparable fault-detection effectiveness to other ART algorithms for OO testing (according to the F-measure, or the expected number of test case executions required to find a failure). Furthermore, the test case generation overhead of L′OP-ART is low, close to that of RT.

THE RELATIONSHIP OF JOB FACTORS AND CLOSE READING HABITS WITH MYOPIA SUFFERING IN TEENAGERS

Based on the concept of static refraction, myopia is a condition in which parallel rays entering the eyeball are refracted by the refractive media in front of the retina. As a result, objects that are far from the eyeball do not appear clearly in the patient's vision. The inability of a person to see distant objects clearly can occur, among others, due to heredity, work and reading habits or position, work environment, etc. To overcome refractive errors in myopia sufferers, minus spherical correction lenses can be given in front of the patient's eyes, so that the light that falls in front of the retina can be shifted and focused right on the retina. The purpose of this study, the authors want to explain the existence of a mutually influencing relationship between near work vision and close reading habits, as well as room lighting used by people with myopia. This research was conducted using a quantitative research method with a sampling technique "systematic random sampling". The results of this study found that myopic refractive errors occupied the highest percentage, namely 65.5%, that there was a mutually influencing relationship between work vision and close reading habits, as well as room lighting used by people with myopia

Microlidar Application for Object Detector to Support The Navigation System in Self-Driving Vehicle

The ability to detect and measure the distance of potential obstacles is importance for navigation system in self-driving vehicles. The measurement process needs to be fast and accurate since the controller requires real-time data to make quick decisions and respond to any potential disturbances on the vehicle's track. This research aims to develop Microlidar for detection system that can accurately measure the distance of a potential obstacle object. The Microlidar utilize Lidar Lite V3 proximity sensor which have range measurement specification of up to 40 meter. Microlidar rapidly rotate 360 degrees by using a stepper motor while in the same time continuously measure the real-time distance. The measurement data are sent to a microcontroller through I2C, and the Processing software plot the 2D image which work like radar visualization. The system is assessed for ranging the various distance object in static and dynamic measurement mode. The results show that the Microlidar has a good level of accuracy with an average error value at the distance of 300 cm is 4.99 cm or 1.7% while the average error value at the distance of 1000 cm is 15.69 cm or 1.6% obtained from 100 data sets collected. The communication from the sensor to Arduino requires a minimum baud rate of 115200 bits/second to minimize data loss and ensure that the measured distance can be processed in real time by the microcontroller. Real-time data with high speed is essential since it will be used on the vehicle in order to quickly decide whether there is a barrier or not on the vehicle’s track. The sensor analysis distance expected from this research could be used as a reference to support the navigation system performance of self-driving vehicles.

A Novel Method for Full‐Field Scale Factor Calculation under Off‐Axis Measurements for Vision‐Based Structural Multipoint Displacement Measurement and Health Monitoring

For vision‐based structural displacement measurement and health monitoring, the scale factor (SF) calculation plays a pivotal role in converting the pixel displacement into the actual one. On the other hand, for the current SF calculation methods, the object distance, between the to‐be‐measured points on the object plane and the optical center of the shooting instrument, has to be measured in advance due to the existence of the pitch and horizontal angle under off‐axis measurement. Unfortunately, it is usually inefficient, difficult, and even impossible to obtain the object distances for all the to‐be‐measured points, especially for the full‐field displacement monitoring on huge scale structures. In this paper, a novel SF calculation method was proposed to calculate the full‐field scale factor of the host structure under off‐axis measurement by combining with the similarity relation of the camera imaging model. With the help of this method, all the scale factors of the to‐be‐measured points can be calculated accurately and highly efficiently, as long as the object distance of any one point or the geometric dimensions of any feature object on the object surface is provided. In addition, to quantitatively assess the SF calculation accuracy of the method, a static vision‐based measurement investigation was firstly conducted, and then, two experimental investigations about the multipoint and full‐field structural displacement measurement on a cable‐stayed bridge model and a simply supported beam model were conducted to validate the effectiveness and feasibility of the proposed method. Finally, all the results demonstrated that the proposed method exhibits an excellent performance on the SF calculation under off‐axis measurement and provides a great potential to be utilized for the vision‐based structural multipoint and full‐field displacement measurement and health monitoring.

“Visibility” at Italian natural parks: preliminary data from the first-ever pilot project by ENEA and CUFAA

“Visibility” is meant as the greatest distance at which an observer can see a distant object in contrast with the horizon and, when referred to a landscape, it depends on the optical characteristics of the atmosphere, somehow linked to the presence of air pollutants. Visibility can hence become a useful indicator of air quality and the project Visibility, representing the first case of visibility monitoring in Europe, aims to apply this approach within the Italian National Parks as natural areas with a significant fruition vocation. The goal is to test the U.S. National Park Service protocol I.M.PRO.V.E. (Interagency Monitoring of PROtected Visual Environment) for the quantification of a coefficient that describes the light extinction (Bext) as a function of different chemical-physical parameters associated with molecules dispersed in the air, and the Circeo National Park (LT) was selected for the “pilot” action, with the positioning of the measuring instruments for atmospheric pollution evaluation at the Lago dei Monaci site. 24-hours samplings were performed in spring-summer 2021 and winter 2021/2022, to measure the air concentration of PM2.5 and PM10 particulate matter, sulfates and nitrates, elemental carbon and organic carbon, metals and trace elements and the gaseous species nitrogen dioxide, with the consequential calculation of the Bext. The visual detection of the degree of air transparency was performed by a panoramic camera pointed in the direction of the “landmark” Monte Circeo, taken as a reference for the definition of long-distance visibility.

Photometric analysis of Type Ia Supernova 2023jvj

The study of supernovae – large explosions at the end of a star’s life that release immense energy and various elements into space – is important in the fields of stellar evolution and the expansion of the universe. In the large cosmological time scale, supernovae allow for a short glimpse into the universe’s evolution. Upon the discovery of Supernova (SN) 2023jvj, located in an anonymous galaxy in the Boötes constellation, our team launched an investigation to further knowledge of transient objects and their role in the cosmos. In this study, we conducted a photometric analysis of SN 2023jvj to determine its type and distance. We hypothesized SN 2023jvj would be a Type Ia supernova based on reports that there were no hydrogen spectral lines. We observed SN 2023jvj and collected photometric data over four weeks using the 12-inch Meade Schmidt Cassegrain Telescope (SCT) and 16-inch Ritchey-Chretien Telescope (RCT) at the Leitner Family Observatory and Planetarium (LFOP). Images were additionally collected remotely from Australia and Utah via the iTelescope Network. Using this data, standard Sloan green and Sloan red magnitudes were found through color calibration and used to generate a light curve (a plot of supernova brightness over time). SN 2023jvj was then determined to be a Type Ia supernova — a standard candle — with a distance modulus of 35.36. Therefore, SN 2023jvj is approximately 1.246e8 parsecs away from Earth. Our findings have a broader impact on how astronomers analyze the relationship between the brightness and distance of transient objects in the ever-expanding universe.

A verification on accuracy of 3D models constructed by SfM software—In terms of reconstructing shape and measuring distance of objects and sites—

A verification on accuracy of 3D models constructed by SfM software—In terms of reconstructing shape and measuring distance of objects and sites—

Error assessment of subjective estimates of linear breast dimensions versus the objective method.

The study aimed to investigate the subjective method of estimating linear breast dimensions in comparison to the objective method. The reproducibility and accuracy of the subjective method of estimating linear breast dimensions during a simplified breast shape analysis were examined. Four linear breast dimensions including the distance from the sternal notch to the nipple, distance from the nipple to the inframammary fold, distance from the nipple to the midline and under-breast width were evaluated based on subjective estimates. Images from 100 women with natural breasts and without any history of breast surgery were reviewed by two examiners three times each. The cases were obtained from a large database of breast images captured using the Vectra Camera System (Canfield Scientific Inc., USA). The subjective data were then compared with the objective linear data from the Vectra Camera System in the automated analysis. Statistical evaluation was conducted between the three repeated estimates of each examiner, between the two examiners and between the objective and subjective data. The intra-individual variations of the three subjective estimates were significantly greater in one examiner than in the other. This trend was consistent across all eight parameters in the majority of the comparisons of the standard deviations and variation coefficients, and the differences were significant in 14 out of 16 comparisons (p<0.05). Conversely, in the comparison between the subjective and objective data, the estimates were closer to the measurements in one examiner than the other. In contrast to the reproducibility observed, the assessment of the accuracy revealed that the examiner who previously presented with less reproducibility of the estimated data overall showed better accuracy in comparison to the objective data. The overall differences were inconsistent, with some being positive and others being negative. Regarding the distances from the sternal notch to the nipple and breast width, both examiners underestimated the values. However, the deviations were at different levels, particularly when considering the objective data from the Vectra Camera System as the gold standard data for comparison. Regarding the distance from the nipple to the inframammary fold, one examiner underestimated the distance, while the other overestimated it. An opposite trend was noted for the distance from the nipple to the midline. There were no differences in the estimates between the right and left sides of the breasts. The correlations between the measured and estimated distances were positive: as the objective distances increased, the subjective distances also increased. In all cases, the correlations were significant. However, the correlation for the breast width was notably weaker than that for the other distances. The error assessment of the subjective method reveals that it varies significantly and unsystematically between examiners. This is true when assessing the reproducibility as well as the accuracy of the method in comparison to the objective data obtained with an automated system.

YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. This urgent task, however, faces challenges, such as the complex working environment and the considerable amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies YOLO-CSM as a suitable solution for small and distant object detection that outperforms several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.

Analysis of the Use of Optical Waves in the World of Health: Literature Study

Waves are vibrations that propagate at any time. Waves are divided into two, namely mechanical waves and electromagnetic waves. In the world of health, what we often encounter are tools whose use is accompanied by one of the physics concepts, namely optical waves. Optical waves in the world of health are very important to apply because they are the basis for learning in health science or in the sense that the concepts that are developed in health science use the concept of optical waves. Optical waves are known as electromagnetic waves, because these waves describe visible light rays, infrared, and ultraviolet rays, so these rays are used as materials for the use of optical instruments. There are many kinds of optical instruments, including loupes which are used to magnify small things, microscopes as a tool to see objects that are far away with the sense of sight, binoculars as a tool to see the distance of objects that are far from the surface of the earth, and the eye as a tool for our vision. Then scientists applied the wave concept with optical tools to serve as a reference in the world of health. Waves according to Prof. Yohanes Surya, Ph. in the title of the book Vibrations and Waves (2019), propagating vibrations produce energy and move at a certain speed, and do not drag the material they pass through. The use of optical waves is widely applied in medical science, including X-rays as a detector for internal body elements to clarify broken parts such as bones or other things. From the explanation above, it can be concluded that waves are the basis in the world of health, which are of course used in optical equipment, and are a reference for the health team and as a basic concept in physics learning. This underlies the fact that optical waves are not only used in the world of education but are very It is also needed in the world of health.&#x0D;

Structured illumination and image enhancement of three-dimensional and moving objects at a distance via incoherent Fourier ptychography.

We experimentally apply incoherent Fourier ptychography to enhance the resolution of recorded images by projecting known, uncorrelated, random patterns at high speed onto 3D moving and distant objects. We find that the resolution enhancement factor can be greater than 2, depending on the projection and camera optics.

Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region

CCTVs are commonly used for traffic monitoring and accident detection, but their images suffer from severe perspective distortion causing object size reduction with distance. This issue is exacerbated in tunnel CCTVs, positioned low due to space constraints, leading to challenging object detection, especially for distant small objects, due to perspective effects. To address this, this study proposes a solution involving a region of interest setup and an inverse perspective transformation technique. The transformed images, achieved through this technique, enlarge distant objects, maintaining object detection performance and appearance velocity across distances. To validate this, artificial CCTV images were generated in a virtual tunnel environment, creating original and transformed image datasets under identical conditions. Comparisons were made between the appearance velocity and object size of individual vehicles and for deep learning model performance with multiple moving vehicles. The evaluation was conducted across four distance intervals (50 m to 200 m) from the tunnel CCTV location. The results reveal that the model using original images experiences a significant decline in object detection performance beyond 100 m, while the transformed image-based model maintains a consistent performance up to the distance of 200 m.

ScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Large-Scale Scene Rendering

High-quality large-scale scene rendering requires a scalable representation and accurate camera poses. This research combines tile-based hybrid neural fields with parallel distributive optimization to improve bundle-adjusting neural radiance fields. The proposed method scales with a divide-and-conquer strategy. We partition scenes into tiles, each with a multi-resolution hash feature grid and shallow chained diffuse and specular multilayer perceptrons (MLPs). Tiles unify foreground and background via a spatial contraction function that allows both distant objects in outdoor scenes and planar reflections as virtual images outside the tile. Decomposing appearance with the specular MLP allows a specular-aware warping loss to provide a second optimization path for camera poses. We apply the alternating direction method of multipliers (ADMM) to achieve consensus among camera poses while maintaining parallel tile optimization. Experimental results show that our method outperforms state-of-the-art neural scene rendering method quality by 5%--10% in PSNR, maintaining sharp distant objects and view-dependent reflections across six indoor and outdoor scenes.

THE PROBLEM OF OBTAINING COMBINED GAMMA AND OPTICAL DETECTORS FOR THE REGISTRATION OF FAST NUCLEAR PROCESSES

The physical and technical aspects of the registration of fast physical processes involving nuclear transformations with the help of binary detectors in the γ-and optical ranges are considered We chose a semiconducting perovskite crystal CsPbBr3 as the main element of the detector. Have been presented with geometrical and technologically usable parameters of the CsPbBr3 crystal for its implementations in nuclear medicine, geophysics and astrophysics. One of the objects that allow testing of the developed high-speed spectrographic equipment are lightning discharges in the atmospheres of planets, including the Earth. The thermonuclear nature of γ-bursts detected during thunderstorms was revealed by their spectra. The paper shows the role of the corresponding channels involving high-energy protons and α-particles, leading to the formation of 116C, 137 N, 158O isotopes. The registration of the γ-spectrum of the flash and its evolution allowed to estimate the character, energy and time scales of the processes necessary for the design and manufacture of multipurpose measuring complexes by us. The inclusion of γ-spectra in the consideration allowed to estimate the correlation between the maximum currents of particles and the productivity of γ-rays. In the experiments planned by us, the magnetic field fluctuations caused by currents are simultaneously recorded by highly sensitive magnetic field detectors. The height of the building of the Faculty of Physics in Smolyan, Bulgaria, is 900 meters above sea level. This makes it possible to place the measuring complex as close as possible to the sources of hard radiation and to carry out easurements in the immediate vicinity. Unlike distant space objects, the perovskite detector registers the positions themselves. This makes it possible to use the methods of positron γ-spectroscopy and accurately determine the parameters of local currents. The technological parameters of the device were determined. A simulation model was created in Simulink MATLAB, LabVIEW with synchronization of the operation of the listed spectrographs. The characteristic shape of the signal formed by individual γ-quanta with the parameters of the Gaussian function and the total number of these quanta are calculated. The degree of mathematical blinding of neighboring Gaussian functions and its influence on the structure of the final spectrogram in the form of an autocorrelation function is estimated. The similarity of the time scales of thermonuclear explosion processes on white dwarfs (WD) and the processes of synthesis of 116C, 137 N and 158O isotopes in the flash head is determined. It is concluded that it is expedient to create a robotic network of lightning observation stations similar to the meteor patrol at the I. I. Mechnikov National University of Ukraine.

For higher accuracy: 3D eye movement gaze depth estimation method based on Mix-TCN

Currently, the biggest obstacle in applying eye tracking technology in Virtual Reality (VR) and Augmented Reality (AR) scenes is the particular difficulty in choosing distance and object distance in 3D scenes. In previous research, geometric calculation methods using vestibular ocular reflex (VOR) and research on binocular visual angle have been studied to some extent, but unfortunately, their effects have not reached a practical level. The paper proposes a new research idea to estimate the depth of binocular gaze using a method of time series eye movement data analysis based on depth learning, and proposes a Mix-Temporal Convolutional Network (TCN) optical time series network. By combining VOR and deep learning theory, the paper has realized the current state-of-the-art technology for estimating the depth of gaze from the movement of the gaze.

Distant object sensing through tap water with enhanced detection range using a passive microwave resonator and wideband impedance integral

Distant object sensing through tap water with enhanced detection range using a passive microwave resonator and wideband impedance integral

Movement tracking using asymmetric impedance meta-surface based on Helmholtz resonator

Acoustic sensors are one of the most commonly used sensors for detecting obstacles in mobile robots and vehicles by providing distance information of the obstacles in their path. However, although a single sensor can determine an object's distance, two or more sensors must be used to detect the movement of an object. Here, we propose a method to determine the distance and direction of an object traveling to a single sensor using an asymmetrically formed acoustic field. The acoustic field is formed by impedance-varying acoustic meta-surface using an asymmetric Helmholtz resonator array. We present multi-physics simulations using COMSOL and experimental results which show the movement of an acoustic source using a single microphone. Also, we present, explicitly, the effective material parameter variation made by the meta-surface with varying degrees of internal neck lengths.

Neural Radiance Fields-Based 3D Reconstruction of Power Transmission Lines Using Progressive Motion Sequence Images

To address the fuzzy reconstruction effect on distant objects in unbounded scenes and the difficulty in feature matching caused by the thin structure of power lines in images, this paper proposes a novel image-based method for the reconstruction of power transmission lines (PTLs). The dataset used in this paper comprises PTL progressive motion sequence datasets, constructed by a visual acquisition system carried by a developed Flying–walking Power Line Inspection Robot (FPLIR). This system captures close-distance and continuous images of power lines. The study introduces PL-NeRF, that is, an enhanced method based on the Neural Radiance Fields (NeRF) method for reconstructing PTLs. The highlights of PL-NeRF include (1) compressing the unbounded scene of PTLs by exploiting the spatial compression of normal L∞; (2) encoding the direction and position of the sample points through Integrated Position Encoding (IPE) and Hash Encoding (HE), respectively. Compared to existing methods, the proposed method demonstrates good performance in 3D reconstruction, with fidelity indicators of PSNR = 29, SSIM = 0.871, and LPIPS = 0.087. Experimental results highlight that the combination of PL-NeRF with progressive motion sequence images ensures the integrity and continuity of PTLs, improving the efficiency and accuracy of image-based reconstructions. In the future, this method could be widely applied for efficient and accurate 3D reconstruction and inspection of PTLs, providing a strong foundation for automated monitoring of transmission corridors and digital power engineering.