Camera System Research Articles

A 3D Surface Reconstruction Pipeline for Plant Phenotyping

Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source.

Automatic Leaf Health Monitoring with an IoT Camera System based on Computer Vision and Segmentation for Disease Detection

Manual identification of diseases in crops is costly and subjective, driving the need for automated systems for accurate detection in the field. This requires the use of technologies based on the integration of IoT and deep learning models to improve the assessment capacity of crop health and leaf disease, with continuous monitoring. The literature review highlights technological solutions that include weed and disease detection using artificial intelligence and autonomous systems, as well as semantic segmentation algorithms to locate diseases in field images whose processes can be improved with systems based on microcontrollers and sensors. This research implements a leaf health monitoring system using IoT and AI technologies, with the development of an IoT device with a camera, the configuration of an MQTT broker in NODE-Red, and the implementation of a script in Python for leaf instance segmentation and image display. As a result, it is highlighted that image analysis, with the Python tool, allowed obtaining valuable information for precision agriculture, while the visualization or messaging interface allows health monitoring and management of crops. In conclusion, the System adequately performs image capture, processing, and transmission, being a contributes to precision agriculture solutions, considering that this can be improved with the integration of more complex deep learning algorithms to increase precision.

The LimPa mission: a small mission proposal to characterize the enigmatic lunar dust exosphere

The lunar environment is known to be characterized by complex interactions between plasma, the exosphere, dust, and the surface. However, our understanding of the environment is limited due to the lack of experimental evidence. Here, we propose a small, low-cost mission to characterize the dust and exosphere environment of the Moon. Named the Limb Pathfinder (LimPa), this is a proof-of-concept mission aimed toward understanding the coupling between plasma, dust, and tenuous neutral atmosphere. The LimPa mission was proposed to a call for the Small Mission to the Moon issued by European Space Agency in 2023. LimPa is designed to examine the dust exosphere above the lunar polar regions by using an utterly novel remote-sensing technique to measure the solar wind hydrogen atoms—the solar wind protons that are neutralized to hydrogen atoms. Its goals are (1) to detect for the first time the neutralized solar wind hydrogen produced by exospheric gas and levitated dust; (2) to measure the height profiles of the levitated dust and exospheric gas densities; and (3) to determine the emission mechanism of the horizon glow. Our baseline design of the LimPa mission is a 12U CubeSat. Three highly matured instruments are used: an energetic neutral atom camera, a proton sensor, and a camera system. The LimPa CubeSat is proposed to be inserted into a circular lunar polar orbit, with an altitude of 100 km as a baseline. The Sun-pointing attitude will allow measurements of neutralized solar wind that are produced by the exosphere and dust grains above the polar regions. The nominal lifetime is for 3 months as a pathfinder mission. The LimPa mission will open a new window to remote characterization of the lunar dust exosphere environment above the poles, and will demonstrate that this monitoring can be achieved with a simple and low-cost instrument system and spacecraft operation. The concept to be proven by the LimPa mission will enable long-term monitoring of the fragile dust exosphere environment, which substantially impacts on lunar exploration and will be significantly altered by human activities.Graphical abstract

A prospective study on the enhancement of surgical safety in robotic surgery: The BirdView camera system.

To investigate the surgical safety and benefits of using the BirdView camera system with a wide field of view in robotic surgery for rectal cancer in a prospective clinical study. This study included 20 consecutive patients who underwent robotic surgery at our institution between the years 2022 and 2023. The primary endpoint was perioperative safety, which was defined as the occurrence of adverse events, including other organ injuries and malfunctions, caused by the BirdView camera system. There were no injuries to any other organs caused by the console surgeon or assistant forceps during surgery. Surgical adverse events occurred in five cases (atelectasis, paralytic ileus, and anastomotic leakage) during the postoperative course. There were no cases of device failure or damage to the surrounding organs, including peritoneal heat damage. We believe that the BirdView system could be valuable in improving the safety of robotic surgery by enabling the observation of blind spots, thus preventing harm to other organs.

Current Practices and Perceptions of Indocyanine Green Fluorescence Guided Liver Surgery: Insights from a Survey of Hepato-Pancreato-Biliary Surgeons in the United Kingdom.

Indocyanine Green (ICG) fluorescence-guided surgery (I-FIGS) is increasingly being used in hepato-pancreatico-biliary (HPB) surgery. However, the true benefit of I-FIGS, the optimum dosing, and the timing of ICG administration still need to be determined. To conduct future research studies in the above areas, it is essential to understand the current I-FIGS practices among surgeons. This survey investigated the practices and perceptions of I-FIGS in liver surgery among HPB surgeons in the United Kingdom (UK). A survey was sent via email and social media to surgeons from all HPB units in the UK. The survey consisted of 18 questions, covering various aspects such as experience levels, volume of operations, approach to liver resections, ICG dosage, timing of administration, application specifics, camera systems used, and willingness to participate in future trials. The survey was sent to 81 HPB surgeons (working in 25 HPB units) across the UK. The response rate was 70% (57/81 surgeons). 56% of the surgeons reported having the infrastructure for I-FIGS at their hospital. The use of I-FIGS varied in duration and patient volume, with 47% of surgeons reporting its use for less than one year and 53% of surgeons reporting using it in fewer than ten patients. Preferences for the dose and timing of ICG administration also varied, reflecting the absence of standardised guidelines. The Storz camera system emerged as the most used imaging system (42% of surgeons), followed by the Stryker (25.8%). None of the surgeons reported any I-FIGS-related side effects. Ninety-six per cent of surgeons expressed interest in participating in future clinical trials in the field of I-FIGS. The survey highlights that I-FIGS in liver surgery is not widely used in the UK. There are also wide variations in the dosing and timing of ICG administration. Large multi-centre studies are needed to focus on dosing, timing of ICG administration, and establishing its actual role in liver surgery.

From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage.

Biomimetic Active Stereo Camera System with Variable FOV

Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static calibration based on camera rotation angles with dynamic updates of extrinsic parameters, the method leverages relative pose adjustments between the rotation axis and cameras to update extrinsic parameters continuously in real-time. It facilitates epipolar rectification as the FOV changes, and enables precise disparity computation and accurate depth information acquisition. Based on the dynamic calibration method, we develop a two-DOF bionic active camera system including two cameras driven by motors to mimic the movement of biological eyes; this compact system has a large range of visual data. Experimental results show that the calibration method is effective, and achieves high accuracy in extrinsic parameter calculations during FOV adjustments.

In Vivo Acquisition of Human Retinal Double-Pass Images during Simulated Intraocular Lens Implantation.

The aim of the study was to capture images that form on the human retina after the simulated implantation of an intraocular lens (IOL). White light was used rather than the commonly used near-infrared light, which is unsuitable for the examination of diffractive IOLs. For this purpose, a special optical setup was developed to investigate the influence of the IOL design on two-dimensional retinal images in vivo. A double-pass ophthalmoscopic setup with a scientific CCD camera system was developed. Imaging the retinal image of a white LED located at infinity provides access to the double-pass point spread function of the natural eye. Subsequently, a see-through device for simulated IOL implantation (VirtIOL, 10Lens S. L. U., Terrassa, Spain) was integrated to investigate the influence of the IOL design on the retinal image quality of complex scenarios. Retinal images were acquired from an incoherent white point light source. Combined with simulated IOL implantation, retinal images were acquired from the point light source, letters, and a United States Airforce target on a 6-m distant monitor. As expected, the double-pass images obtained with a monofocal IOL were sharper than those obtained with a multifocal IOL. The method opens up access to double-pass point spread function for white light, thus solving the problem of infrared light-based methods providing incorrect results when examining diffractive IOLs. This approach may be helpful for the investigation of perception in the future.

Characteristic evolution and energy variation during the generation of water droplet

Understanding water droplet characteristic is an important prerequisite for improving wet dust removal efficiency. Using the high-speed camera system, the process of water droplet generation under the different Ca2+ concentrations and injecting velocities was studied. The width and length of water droplet increased, whereas the ratio of droplet width and length decreased with generation time. The water droplet generation time decreased with injecting velocity increasing, whereas kept almost unchanged with Ca2+ concentration. The equivalent diameter of droplet decreased with injecting velocity, whereas presented first a slight decrease and then a slight increase with Ca2+ concentration. This result suggested that the injecting velocity effect was stronger than the Ca2+ effect on the water droplet generation time and size. Furthermore, the effective injecting force and capillary force were mainly forces to influence the droplet generation in force analysis. RF (ratio of capillary force and effective pressure force) was first used to evaluate the synergistic effect of capillary force and effective injecting force. The greater RF, the water droplet generation time was longer and water droplet diameter was larger. Furthermore, the relationship between surface energy per unit (E/S) of water droplet and RF was a negative correlation. Those results can provide valuable suggestions to the development theory of dust removal.

Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring

The increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency of feeding methods, identify health problems or environmental stressors, and help implement sustainable practices. The application of cameras enables remote monitoring of various environments and activities in real time, providing quick insights, improving security measures, and facilitating decision-making based on the collected data. This paper proposes a low-cost camera system and an image processing-based strategy to monitor and measure the size of Pacific oysters inside a tank. The fast segment anything model algorithm is implemented in the Python programming language to perform image segmentation and generate object masks for subsequent processing. The size of oysters is obtained by measuring the distance between the parallel lines of the bounding box that is generated surrounding them. The largest absolute errors and relative errors for the estimates were 3 mm and 5.13%, respectively, when compared to measurements made manually. Furthermore, the width and height have standard deviations of 0.6 and 0.3 mm, respectively, indicating low data dispersion and good repeatability of the algorithm. Since the accuracy of the human eye in estimating a dimension without the aid of a distance measuring instrument tends to be lower than that obtained by the proposed tool, it is believed that the approach can significantly contribute to the management of oyster farm production, providing strategic insights based on animal growth for producers.

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

Wavefront detection and reconstruction method for active optical system of large FOV space camera based on self- reference light source

Wavefront detection and reconstruction method for active optical system of large FOV space camera based on self- reference light source

Usability of Mobile Control Room and CCTV Systems for Crowd Control at Large-Scale Events with the Occurrence of a Large Number of People With Focus on the Czech Republic

This article deals with the issue of the use of modern instruments for crowd control, in particular the use of a mobile closed-circuit television system connected directly to the control room during largescale events. The article has two aims: to check the usability of this type of system especially from the perspective of increasing the safety of event visitors, and to identify the optimal technical solution for crowd monitoring and management and possible obstacles for deployment in the field. The article further focuses on the practical usability of the information obtained through the mobile camera system and the control centre for increasing the safety of event visitors from the perspective of security managers. The conducted investigation identified obstacles to the deployment of a mobile camera system as well as described the optimal parameters for the use of a mobile camera system, and it collected feedback from the security team participating in the security management of the event. The results of the contribution enable easier orientation in the selection of suitable hardware and software for crowd control; Key vulnerabilities in deploying mobile CCTV systems were identified, including issues such as data transmission stability over LTE networks and the financial costs of alternative solutions like satellite connections. Based on field testing and feedback from security teams, recommendations were made to improve the system’s effectiveness and adaptability, leading to more precise decision-making and proactive risk management at large-scale events.

The generation method of orthophoto expansion map of arched dome mural based on three-dimensional fine color model

Murals carry cultural significance and historical information, and are an important channel for understanding ancient social norms, artistic styles, and religious beliefs. At present, the digitization of murals is an important technical means for the protection of cultural heritage. Orthogonal images of murals play a vital role in high-precision recording, preservation, academic research, educational expansion, mural protection, digital exhibition and dissemination. At present, orthogonal images of murals are mostly realized by plane projection, but this method is not suitable for making orthogonal images of arched and dome-shaped murals. To address this problem, this paper proposes a method for generating orthogonal expansion images of arched and dome-shaped murals. This method combines a three-dimensional virtual space simulation model with an RTT virtual camera and adopts a spatial reference orthogonal ray scanning model. First, the detailed three-dimensional color model is fitted to the geometric reference of cylindrical and spherical objects to determine its parameters. Next, for the cylindrical murals on the arch, the orientation of the model is initialized using quaternions, and the viewport matrix is adjusted to obtain the required resolution. Then, the RTT camera is used to perform line orthogonal projection in the viewport, and the fringe projection image is generated by rotating around the cylinder axis according to the inversely calculated rotation angle. For the murals on the dome ceiling, this method is used to segment them according to a certain longitude, and the circumscribed cylinder of the fitted sphere is rotated to perform cylindrical orthogonal line scanning in the segmented area. These individual orthogonal line scan images are carefully spliced together to form a complete orthogonal unfolded image. Finally, a fringe projection image is generated with the central meridian of the unfolded part as the center line, and the fringe projection images are spliced together to obtain the final orthogonal unfolded image. Experiments show that compared with existing methods, this method can generate two-dimensional orthogonal unfolded images with high texture fidelity, minimal texture deformation, and uniform deformation distribution. This study provides a novel perspective on the orthogonal unfolding of quasi-cylindrical and quasi-spherical painted objects, and provides an accurate and diverse data basis for the digitization of murals.

A pilot prospective study of forward-looking infrared (FLIR) camera measurements to predict postoperative wound complications in high-energy lower extremity fractures.

To prospectively determine if forward-looking infrared (FLIR) camera temperature measurements can predict postoperative wound complications in high-energy lower extremity fractures. This is a prospective cohort study from a single level 1 trauma centre. Adult patients who sustained unilateral high-energy lower extremity fractures (tibial plateau, tibial plafond, trimalleolar ankle, midfoot, or calcaneus fractures) were included in the study. Preoperative and post-induction FLIR thermographic measurements were taken using the FLIRONE ® PRO camera system. The main outcome of interest was the development of any wound related postoperative complication. Forty-eight patients were included in the study. A majority of the patients were male (58%) with a mean age of 44.2years. FLIR imaging detected temperature differences between the operative extremity and the contralateral extremity, both in the preoperative area and following induction (Pre-op: 33.0 vs 30.8, p < 0.001; Post-induction: 29.6 vs 28.5, p = 0.046). Overall, 11 (23.9%) patients experienced a wound complication. Regression analysis did not demonstrate a significant association between preoperative or post-induction FLIR measurements and the development of wound complications. While FLIR imaging could detect temperature changes related to traumatic injury, these differences did not correlate with postoperative outcomes. Further large-scale study may be warranted. III.

Protocol of a cluster-randomised trial to improve adolescent bicycling safety education programme efficacy

IntroductionSecond to motor vehicles, bicycles contribute to more childhood injuries than any other consumer product. Youth bicycling safety education programmes are one avenue of prevention, but despite a plethora of...

Spatiotemporal Trajectories of Pedestrian Mobility at the Train Station: evidence of 24 million trajectories

Understanding pedestrian movement remains crucial for designing efficient and safe transportation structures such as terminals, stations, or airports. The significance of conducting a granular analysis in pedestrian mobility dynamics research is evident in refining crowd behavior modeling. It is essential for gaining insights into potential terminal layouts, crowd management strategies, and evacuation procedures, all of which enhance safety and efficiency. In this context, we offer an original empirical dataset of 24,000,000 samples of trajectory spatial movement at traffic terminals in Havlíčkův Brod and Pardubice, Czech Republic. The dataset was collected using a high-resolution camera system installed at the railway station. Subsequently, algorithmic post-processing was applied to extract anonymous data on the spatial movement of recorded pedestrians. Thanks to this dataset, researchers can delve into the distances between pedestrians in a transportation terminal, considering factors such as group composition, group-to-group distances, and movement speed.

A study to determine the three-dimensional (3D) facial shape characteristics for a successful FFP3 mask fit

A reported 20% of dental staff will fail their fit test for a disposable FFP3 respirator. This needs to be factored into future pandemic workforce and PPE supply planning. At present there are no scientifically or universally accepted facial shape criteria to design and produce facial masks that will fit the entire work force. This study presents differences in facial shape, volume and surface area between individuals who passed on several FFP3 masks (pass group) and participants who passed on only one FFP3 mask (fail group). Three dimensional images of 50 individuals, 25 in each group, were taken at rest and at maximum smile using a DI4D SNAP 6200 camera system. The images were processed, and four “average faces” were produced—pass group at rest, fail group at rest, pass group at maximum smile and fail group at maximum smile. Simple Euclidian linear and angular measurements, geodesic surface distances and volume and surface area enclosed within the mask were analysed. The results of the study show that individuals who are more likely to pass a mask fit test have longer faces, wider mouths, greater geodesic surface distances and a greater volume and surface area of soft tissue enclosed within the mask boundary. This would suggest that some manufactures masks may be too large, and they need to reduce the size of their masks or produce a category of sizes, accepting the fact that one size does not fit all.

SuMOS, a submerged microscope for observing substrates: Studying benthic activity in aquatic environments

Described here is the construction of a low-cost, standalone underwater camera system designed for recording processes occurring on aquatic substrates. The Submerged Microscope for Observing Substrates (SuMOS) utilizes a Raspberry Pi Zero 2 W paired with a Raspberry Pi Camera Module v3 NoIR, with IR illumination for low-light situations. It features a waterproof housing inspired by the open-source PipeCam project, with enhancements for sealing and substrate mounting. The SuMOS system operates autonomously, capturing high-resolution images at fixed intervals under various lighting conditions. Tests in the Choptank River of Maryland demonstrated the system’s robustness in capturing patterns of amphipod activity under challenging optical conditions. This versatile tool offers a scalable solution for highly time-resolved, in situ studies of processes occurring at the interface of the aquatic/solid surface boundaries. The SuMOS provides significant advantages in cost, ease of deployment, and data collection.

Binocular camera calibration using a BP algorithm optimized by an improved subtraction-average-based optimizer

The BP algorithm exhibits drawbacks such as reduced precision and extended iteration duration during the process of calibrating a binocular camera system. A technique employing an improved subtraction-average-based optimizer to optimize the BP algorithm (ISABO-BP) is introduced to address these limitations and complete the calibration of the binocular camera. The pixel values of the corner in 2D space and the coordinate values in 3D space are used as the input and output for the BP algorithm, respectively. First, the logistic chaotic mapping is used to initialize the population to increase the diversity of the initial population. The piecewise mapping is employed to generate random values to replace the coefficient values in the individual position update formula, making the population distribution more uniform. The golden sine strategy is adopted to help particles escape from local optima. The ISABO-BP algorithm is utilized to perform the process of calibrating a binocular camera. Then, through numerical experiments, the mean calibration precision for the BP algorithm is 0.1501 and 0.0445 cm for the ISABO-BP algorithm. The calibration precision has been enhanced by 70.4%. The iterative steps of the BP algorithm are 353 epochs, while the ISABO-BP algorithm are 76 epochs, and the iterative speed is increased by nearly four times. It is evident that the ISABO-BP algorithm enhances the calibration precision and expedites the convergence rate. Finally, other intelligent algorithms to optimize the BP algorithm for calibrating a binocular camera are compared, and the superiority of this algorithm is also verified.