High-resolution Camera Research Articles

Pixel-level Terrain Processing of the Martian Surface from Moderate Resolution Images of Tianwen-1

The moderate-resolution camera (MoRIC) of the Tianwen-1 orbiter, China’s first Mars exploration mission, is a frame-imaging scheme color camera. However, generating a precise digital elevation model (DEM) from these images faces challenges due to the presence of featureless and repetitive regions on the Martian surface. Traditional photogrammetric methods often struggle to achieve pixel-level resolution in these areas. To address this, we employ a shape from shading (SFS) algorithm that leverages shading patterns to reconstruct fine-scale terrain details, optimizing photogrammetric initial DEM. This paper presents a novel global bisection search algorithm, based on the Pearson correlation coefficient to determine the smoothing parameter, a crucial element in the SFS process. Comparisons with higher-resolution data from the Mars Express high-resolution stereo camera and Mars Reconnaissance Orbiter CTX images validate our approach, highlighting the potential of SFS to significantly enhance the scientific value of Tianwen-1 MoRIC data by generating high-resolution, three-dimensional maps of Mars.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Hybrid Refractive and Diffractive Testing Method for Free-Form Convex Mirror in High-Resolution Remote-Sensing Cameras

The development of high-resolution and large field of view remote-sensing cameras is inextricably linked to the application of free-form mirrors. The free-form mirror offers higher design of freedom and is more effective at correcting aberrations in optical systems. The surface shape error of a free-form mirror directly affects the imaging quality of remote-sensing cameras. Consequently, a high-precision free-form mirror detection method is of paramount importance. For the convex free-form surface mirror with a large aperture, a hybrid refractive and diffractive testing method combining computer-generated holography (CGH) and spherical mirrors for high-precision null test is proposed in this paper. When comparing the effect of error and the detection sensitivity of different designs, the results showed that the influence of the system error is reduced by about 42% and the sensitivity is increased by more than 2.6 times. The proposed method can achieve higher testing accuracy and represents an effective and feasible approach for the surface shape detection method.

Determinación de la densidad vehicular en ciudades intermedias por medio de visión por computador

The objective of this research work is to calculate the vehicle density of cities through the use of computer vision. To carry out the study, we captured videos of vehicular traffic at 8 intersections in the city of Loja using a drone and a high-resolution camera. Subsequently, we used the Python language and the YOLOv8 library to count vehicles of different categories, such as small vehicles, trucks, and motorcycles. Through mathematical formulas used in transportation engineering, the values ​​of vehicle density, vehicle flow rate, and average spacing were obtained. As results, we have that the machine learning model using YOLOv8 has an accuracy of 90% in detecting and classifying vehicles, and thanks to its use, the road with the highest vehicle density was identified. The practical applications of this work could improve vehicle flow and help competent organizations related to traffic management make decisions.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

The Effects of Depth and Altitude on Image-Based Shark Size Measurements Using UAV Surveillance

Drones are an ecological tool used increasingly in shark research over the past decade. Due to their high-resolution camera and GPS systems, they have been used to estimate the sizes of animals using drone-based photogrammetry. Previous studies have used drone altitude to measure the target size accuracy of objects at the surface; however, target depth and its interaction with altitude have not been studied. We used DJI Mavic 3 video (3960 × 2160 pixel) and images (5280 × 3960 pixel) to measure an autonomous underwater vehicle of known size traveling at six progressively deeper depths to assess how sizing accuracy from a drone at 10 m to 80 m altitude is affected. Drone altitudes below 40 m and target depths below 2 m led to an underestimation of size of 76%. We provide evidence that accounting for the drone’s altitude and the target depth can significantly increase accuracy to 5% underestimation or less. Methods described in this study can be used to measure free-swimming, submerged shark size with accuracy that rivals hand-measuring methods.

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

Use of a Head-Mounted Assisted Reality, High-Resolution Telemedicine Camera and Satellite Communication Terminal in an Out-of-Hospital Cardiac Arrest

Mayo Clinic Ambulance Service is testing a novel combination of technologies to enhance the ability to provide prehospital telemedicine connecting physicians with paramedics. Mayo Clinic Ambulance Service partnered with start-up company OPTAC-X to field test a novel head-mounted video camera connected with a satellite communications terminal to bring medical control emergency medicine physicians to the patient and paramedic by video. The authors believe this is the first report of a physician providing medical guidance to paramedics resuscitating an out-of-hospital cardiac arrest using these technologies.

Comparison of thermal cameras and human observers to estimate population density of fallow deer (Dama dama) from aerial surveys in Tasmania, Australia

Context The population of introduced fallow deer (Dama dama) is thought to have increased exponentially across much of the island of Tasmania, Australia, since 2000. Historically, deer management decisions have relied on population trend data from vehicular spotlight surveys. Renewed focus on the contemporary management of the species requires development of more robust and precise population estimation methodology. Aims This study demonstrates two aerial survey methods – conventional counts by trained human observers, and thermal imaging footage recorded during the same flights – to inform future survey practices. Methods Conventional counts were carried out by three observers, two seated on the left side of the helicopter, and one on the right. A high-resolution thermal camera was fitted to the helicopter and was orientated to meet the assumptions of distance sampling methodologies. Both survey methods were used to generate deer population density estimates. Spatial distribution of deer was also analysed in relation to patches of remnant native vegetation across an agricultural landscape. Mark–recapture distance sampling was used to estimate density from human observer counts and provide a comparison to the distance sampling estimates derived from the thermal camera. Key results Human observer counts gave a density estimate of 2.7 deer per km2, while thermal camera counts provided an estimate of 2.8 deer per km2. Deer population density estimates calculated via both methods were similar, but variability of the thermal camera estimate (coefficient of variation (CV) of 36%) was unacceptably high. Human observer data was within acceptable bounds of variability (CV, 19%). The estimated population size in central and north-eastern Tasmania for 2019 approximated 53,000 deer. Deer were primarily congregated within 200 metres of the interface between canopy cover and open pasture. Conclusions The population density estimate provides a baseline for monitoring and managing the Tasmanian deer population. Human observer data was more precise than thermal camera data in this study, but thermal counts could be improved by reducing sources of variability. Implications Improvements for the collection of thermal imagery are recommended. Future control efforts may be more efficient if they preferentially target habitat edges at this time of year, paired with random or grid-based searches where population density is lower.

New insights into the geological evolution history of Mare Fecunditatis

The Luna 16 probe returned 101 g of lunar regolith from the northeast of the Mare Fecunditatis in 1970. Studies on these samples were primarily conducted in the 1970s and 1980s, and limited scientific achievements were obtained due to available technology at that time. China received 1.5 g Luna 16 samples in 2023 and it is expected to conduct in-depth research in the near future. We conducted a thorough investigation on several fundamental issues in this region to provide a refined geological background in the paper. Firstly, a detailed division of the basaltic units within the Mare Fecunditatis was conducted based on the TiO2 content, and impact craters in each geological unit were mapped with the high-resolution Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images. With the method of lunar surface dating from the crater size-frequency distribution, we found that the model ages of these basalt units range from 3.71 Ga to 3.31 Ga. Next, the thickness of the basalt within the Mare Fecunditatis is estimated with different types of craters, and local basalt thickness ranges from 3 m to 304 m from seven rim-completely-exposed craters in the mare. The rim-completely-exposed craters on the mare-highland boundary, the rim-partially-exposed craters in the mare, and rim-completely-buried craters in the maria also provide information on the thickness of local basalt. Finally, numerical simulations of the formation process of the largest young impact crater in this region, Langrenus crater, was conducted. The simulations show that the Langrenus crater could be formed by a 11.2-km-diameter asteroid hitting the lunar surface with a speed of ~10 km/s. The average thickness of the ejecta from the Langrenus crater is ~4.9 m near Luna 16 landing site, indicating that a significant fraction of Luna 16 samples might be the ejecta from the Langrenus crater. The simulations also reveal that the maximum shock pressure on the materials ejected to Luna 16 landing site can reach 92 GPa, and their maximum source depth is approximately 7.1 km. These interpretations can provide valuable information for further study of the Luna 16 samples and inferring the geological history of the Mare Fecunditatis.

Modifying the Silt Lamp by Using a White Laser in Place of the Halogen Light

This paper explores the modification of the slit lamp, a vital ophthalmic instrument, by replacing the traditional halogen light source with a white laser. This modification aims to enhance the diagnostic capabilities and efficiency of the slit lamp. White laser technology offers superior brightness, intensity, and color accuracy, which improves the visibility and differentiation of ocular tissues. Additionally, white lasers are more energy-efficient, provide consistent light output, and require less maintenance. The redesign process includes adjustments to the optical system, incorporating specialized filters and lenses, and ensuring proper thermal management. The integration of a high-resolution digital camera system with white laser illumination further improves image quality and diagnostic precision. The study outlines the benefits, potential challenges, and implementation steps, concluding that the use of white lasers in slit lamps can significantly enhance the effectiveness of ocular diagnostics, benefiting both ophthalmologists and patients.

The Role of Forensic Photography in Criminal Investigation and Trial

Photography is one of the main crucial factors consider during investigation of scene of crime. Crime scene and evidence photography is crucial to investigate a crime scene and of birth in the 21st century round the world all respective friendship, scientist use high resolution camera lens, modern instruments, technology to capture crime scene photos. Evidence collection and capturing it’s photograph and represent in court as vital evidence are benefits of modern photography. In this paper, we will consider the modern aspect of forensic photography in any criminal and civil cases investigation and how it maintains chain of custody.

Evaluating Digital Imaging Technologies for Anogenital Injury Documentation in Sexual Assault Cases

Purpose: Photo-documentation is a critical component of care and an indispensable skill for forensic clinicians treating patients who have experienced violence and trauma. This retrospective study examines the frequency and nature of anogenital injuries identified through colposcope digital imaging in comparison to those detected using a high-resolution camera system. Methods: This retrospective, before-and-after study evaluated genital injuries in all adult women (over 16 years old) who presented to a freestanding Nurse Examiner Clinic (NEC) following sexual assault during a 3-year period. The clinic is supervised by forensic clinicians trained in conducting medical-forensic examinations. In 2016-2017, all injuries were documented using the Cooper Surgical Leisegang© colposcope system, while in 2018, injuries were recorded exclusively with a high-resolution camera system. The primary outcome was the frequency of genital findings documented in sexual assault victims from each group. Results: A total of 367 women were evaluated in the "before" period and 180 in the "after" period. Both groups were similar in terms of demographics, assault history, time to examination, alcohol use, and the occurrence of genital injuries (76.1% vs. 74.9%, p = 0.76). However, patients examined with the high-resolution camera system had a significantly higher number of documented anogenital injuries (2.4 vs. 1.8, p < 0.001). This group also had more anogenital abrasions identified (51.1% vs. 27.0%, p < 0.001). The injury patterns between the two groups were not statistically different. Conclusion: Accurate and reliable photo documentation are the key components of forensic medical documentation. Our findings indicate that the detection of anogenital injuries may differ based on the imaging system used.

Damage investigation of hybrid flax-glass/epoxy composites subjected to impact fatigue under water ageing

The aim of this study is to investigate the fatigue behaviour of hybrid flax-glass/epoxy composites under repeated impact loading subsequent to water ageing. Different plates of these composite materials were fabricated using the vacuum infusion technique. Five stacking sequences were considered: [F8], [G/F3]S, [G2/F2]S, [G3/F]S, and [G8], where F and G stand for flax/epoxy and glass/epoxy plies, respectively. Water ageing was conducted by immersing the composite specimens in tap water at room temperature for various durations, and until saturation was reached. Fatigue impact tests were carried out using three impact energies: 3, 4, and 5 J. An advanced high-resolution camera was used to monitor the evolution of damage mechanisms occurring on the non-impacted surfaces, while a laser thermometer was considered to track the temperature variations within each composite specimen. The obtained results show that flax-glass hybridization reduces the mass of absorbed water in flax/epoxy composite by up to 70%. Furthermore, there is a more pronounced decrease in longitudinal modulus and maximum stress in aged composites, with reductions of up to 70% compared to unaged ones. Additionally, visible damage occurs even at low energy levels, manifesting from the initial impacts in both aged and unaged composite laminates. Moreover, a correlation between the number of impacts to failure and the cumulative energy is revealed. Ultimately, water aging reduces the strength of the studied composite laminates and their resistance to impact fatigue. Furthermore, the hybrid laminates with high proportion of flax layers are particularly susceptible to water ageing effects.

Predicting pedestrian-vehicle interaction severity at unsignalized intersections

Objectives This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models. Methods High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity. Results The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified ‘Vehicle Speed’ as the most significant input variable positively influencing severity. Conversely, ‘Approaching Angle’ and ‘Vehicle Distance from Conflict Point’ negatively impacted severity. Other significant factors included ‘Type of Vehicle’, ‘Pedestrian Speed’, and ‘Pedestrian Yaw Rate’, highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions. Conclusions This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings.

Research on bridge spatial deformation monitoring using light poles and displacement-relay theory

The growing traffic on municipal bridges demands effective methods for monitoring spatial deformations to ensure structural safety. Traditional measurement techniques often fall short in terms of efficiency and precision. This study presents an innovative bridge deformation measurement system that employs light poles and displacement-relay theory, integrated with high-resolution optical cameras, sub-pixel image recognition algorithms, and IMU data. Laboratory experiments involved chain camera units and multiple data processing methods, including sub-pixel recognition and wavelet denoising. Results showed that sub-pixel recognition with four-layer wavelet denoising achieved the highest accuracy, aligning closely matching the performance of commercial equipment. Field tests on the Xinwuyi and Dinghuaimen Bridges under varying traffic conditions confirmed the system’s reliability. The study concludes that the system effectively captures dynamic load variations, enhancing the accuracy of spatial deformation measurements. Future work will refine error analysis and data processing methods to further enhance the system’s practical applicability.

Chandrayaan-3 landing site evolution by South Pole-Aitken basin and other impact craters

The Chandrayaan-3 mission with the Vikram-lander and the Pragyan rover landed in the high latitude highland region near the south pole of the Moon. The landing site is located ∼350 km from the South Pole-Aitken basin rim, an ancient and highly cratered terrain. This site has undergone the complex emplacement sequence of SPA basin ejecta followed by the nearby and distant impact basins and crater ejecta materials. To evaluate the source of individual basin and crater ejecta emplacement over this landing site, we carefully demarcated the nearby and distal basins and craters that could have contributed to the source regolith material. We found that the SPA basin is the major contributor, which deposited nearly ∼1400 m of ejecta materials, and 11 other basins deposited ∼580 m of ejecta. The other complex craters contributed up to ∼90 m of ejecta. Meanwhile, secondary craters of a few km in diameter located adjacent to the landing site contributed to ∼0.5 m ejecta, which are crucial target materials for the Pragyan rover insitu analysis. Pragyan rover images revealed the landing site is devoid of >1 m boulders along the traverse revealing typical highland terrain. The Pragyan rover Navcam and Orbital High Resolution Camera regional images revealed linear distal ejecta rays possibly from the distant impacts as insitu evidence of foreign material at the CH-3 landing site. We found a semi-circular, heavily degraded structure encompassed around the landing site, which is interpreted as a buried impact crater ∼160 km in diameter probably formed before the SPA basin. The erasure of pre-SPA basin craters is caused by both the direct burial by SPA basin ejecta, high seismic shaking during SPA formation, and then followed by various post-SPA craters and its associated some of the degradation processes. Overall, Chandrayaan-3 landed within an ancient region that hosts some of the most deeply excavated materials on the Moon.

Scintillation event imaging with a single photon avalanche diode camera

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator.

Analisis Karakteristik Material Baja dengan Metode Digital Image Correlation (DIC)

The use of steel materials in building construction opens new opportunities for sustainable development, as steel exhibits corrosion resistance, durability, and reliability in terms of strength and ductility. Digital Image Correlation (DIC) is non-contact technique in which digital images of the surface of a test object are captured using high-resolution cameras. This study conducted measurements of strain distribution on the specimen's surface using the DIC method throughout the entire tensile testing process. The study particularly focuses on examining changes in strain distribution during the melting phase and the local deformation phase leading to fracture. In this research, a comparison will be made between the load-displacement curves obtained from experimental laboratory testing and the results analyzed using the DIC method for SS400-grade steel material. Based on the results of the tensile test and DIC analysis that have been conducted, conclusions have been drawn in the research. The tensile test results of SS400 steel material with a thickness of 6 mm, 8 mm, 10 mm, and 12 mm meet the quality requirements in the tested specification standards, and the results of the force-displacement curve between the experimental test results and the DIC method obtained a minimum deviation with a value below 10%,. Therefore, it can be concluded that the DIC method exhibits a reasonably good level of accuracy, making it suitable for validating the results of experimental tests.

TRANSFORMER NETWORKS TO CLASSIFY WEEDS AND CROPS IN HIGH-RESOLUTION AERIAL IMAGES FROM NORTH-EAST SERBIA

The intricate backgrounds present in crop and field images, coupled with the minimal contrast between weed-infested areas and the background, can lead to considerable ambiguity. This, in turn, poses a significant challenge to the resilience and precision of crop identification models. Identifying and mapping weeds are pivotal stages in weed control, essential for maintaining crop health. A multitude of research efforts underscore the significance of leveraging remote sensing technologies and sophisticated machine learning algorithms to enhance weed management strategies. Deep learning techniques have demonstrated impressive effectiveness in a range of agricultural remote sensing applications, including plant classification and disease detection. High-resolution imagery was collected using a UAV equipped with a high-resolution camera, which was strategically deployed over weed, sunflower, tobacco and maize fields to collect data. The VIT models achieved commendable levels of accuracy, with test accuracies of 92.97% and 90.98% in their respective evaluations. According to the experimental results, transformers not only excel in crop classification accuracy, but also achieve higher accuracy with a smaller sample size. Swin-B16 achieved an accuracy of 91.65% on both the training and test datasets. Compared to the other two ViT models, the loss value is significantly lower by half, at 0.6450.