Visible Camera Research Articles

Optimising computer vision-based ergonomic assessments: sensitivity to camera position and monocular 3D pose model

Numerous computer vision algorithms have been developed to automate posture analysis and enhance the efficiency and accuracy of ergonomic evaluations. However, the most effective algorithm for conducting ergonomic assessments remains uncertain. Therefore, the aim of this study was to identify the optimal camera position and monocular 3D pose model that would facilitate precise and efficient ergonomic evaluations. We evaluated and compared four currently available computer vision algorithms: Mediapipe BlazePose, VideoPose3D, 3D-pose-baseline, and PSTMO to determine the most suitable model for conducting ergonomic assessments. Based on the findings, the side camera position yielded the lowest Mean Absolute Error (MAE) across static, dynamic, and combined tasks. This positioning proved to be the most reliable for ergonomic assessments. Additionally, VP3D_FB demonstrated superior performance among evaluated models. Practitioner Summary: This study aimed to determine the most effective computer vision algorithm and camera position for precise and efficient ergonomic evaluations. Evaluating four algorithms, we found that the side camera position with VideoPose3D yielded the lowest Mean Absolute Error (MAE), ensuring precise and efficient evaluations.

Retracted: Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Retracted: Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Low-cost single-shot complex optical field imaging with a simplified aperture

We report a new computational imaging methodology for complex optical field based on a single-frame diffraction intensity pattern acquisition with a simplified aperture and an extremely low-cost industrial machine vision camera. The current design of the apertures for the recovery of complex optical field was suffering from specific coding associated with the use of spatial light modulator or digital micromirror device, where it is difficult to make the optical implementations compact and cost-effective. In this work, we demonstrate that a simplified aperture without any pattern-like coding is efficient to establish the physical constraint for computational reconstruction. The aperture attached to the object defines the imaging region of interest (ROI) with coherent trans-illumination, and only one snapshot of the diffraction intensity pattern is required for recovering the ROI's complex field. Proof-of-concept numerical simulations and optical experiments have been carried out to validate the feasibility, noise robustness, resolution, and potential biological imaging applications of the proposed computational method.

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms.

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.

Examining How Postpartum Videoconferencing Support Sessions Can Facilitate Connections between Parents: A Poststructural and Sociomaterial Analysis.

Postpartum support for new parents can normalize experiences, increase confidence, and lead to positive health outcomes. While in-person gatherings may be the preferred choice, not all parents can or want to join parenting groups in person. Online asynchronous chat spaces for parents have increased over the past 10 years, especially during the COVID pandemic, when "online" became the norm. However, synchronous postpartum support groups have not been as accessible. The purpose of our study was to examine how parents experienced postpartum videoconferencing support sessions. Seven one-hour videoconferencing sessions were conducted with 4-8 parents in each group (n = 37). Nineteen parents from these groups then participated in semi-structured interviews. Feminist poststructuralism and sociomaterialism were used to guide the research process and analysis. Parents used their agency to actively think about and interact using visual (camera) and audio (microphone) technologies to navigate socially constructed online discourses. Although videoconferencing fostered supportive connections and parents felt less alone and more confident, the participants also expressed a lack of opportunities for individual conversations. Nurses should be aware of the emerging opportunities that connecting online may present. This study was not registered.

Stereo vision based systems for sea-state measurement and floating structures monitoring

Using computer vision techniques such as stereo vision systems for sea state measurement or for offshore structures monitoring can improve the measurement fidelity and accuracy with no significant additional cost. In this paper, two experiments (in-lab/open-sea) are conducted to study the performance of stereo vision system to measure the water wave surface elevation and rigid body heaving motion. For the in-lab experiment, regular water waves are generated in a wave tank for different frequencies and wave heights, where the water surface is scanned by the stereo vision camera installed on the top of the tank. Surface elevation inferred by the stereo vision is verified by an installed stationary side camera that records the water surface through the tank transparent side window, water surface elevation measured by the side camera recordings is extracted using edge detection algorithm. During the in-lab experiment a heaving buoy is installed to test the performance of Visual Simultaneous Localization and Mapping (VSLAM) algorithm to monitor the buoy heave motion. The VSLAM algorithm fuses a buoy onboard stereo vision recordings with an embedded Inertial Measurement Unit (IMU) to estimate the 6-DOF of a rigid body. The Buoy motion VSLAM measurements are verified by a KLT tracking algorithm implemented on the video recordings of the stationary side camera. The open-sea experiment is implemented in Lake Somerville, Texas. The stereo vision system is installed to measure the water surface elevation and directional spectrum of the wind generated irregular waves. The open-sea wave measurements by the stereo vision are verified by a Sofar commercial wave buoys deployed in the testing location.

A novel AI‐powered IR‐Visible dual camera system for measuring and tracking ocular surface temperature

Aims/Purpose: To report the development of a novel artificial intelligence (AI) powered, dual camera (infrared (IR)/visible) system capable of measuring and tracking ocular surface temperature (OST) over any time period.Methods: The system consists of an IR camera (Teledyne FLIR IR A655sc) and a visible (V) camera (FLIR BFS 51S5C‐C), co‐mounted on a slit‐lamp for simultaneous and overlapping fields of view (FOV); designed control algorithms; computer hardware and connecting cables. Novel algorithms are leveraged to synchronize IR and V video streams of exposed ocular surface and adnexa for image and video registration. Localisation of the cornea in V video stream obtained by deep learning (DL) AI algorithms designed for semantic segmentation of the cornea. Coordinates of V video stream segmented cornea are then used to extract corresponding OST data from the IR video stream in each video stream frame. Data analysis algorithms are used to extract OST data from IR video stream. Further image segmentation algorithms isolated specific areas of interest (AOI) for OST analysis.Results: The DL algorithm was trained using V images captured using the V camera. Image registration and segmentation errors were calculated to determine accuracy of system. Mean square error for registration was 5.03 ± 1.82 (c.0.45 mm). Mean Intersection over Union (IoU) was 97.6%, representing accuracy in identifying corneal and scleral pixels in tracked eye segmentation. Analysis software extracted rate of OST change and relative OST change compared to baseline across cornea and selected AOI.Conclusions: A novel AI‐powered system for measuring and tracking OST over time was developed. The system synchronously records IR and V video streams of the eye surface and automatically extracts OST over time. The system can track eye movements and remove artefact eyelid blink frames from the data. A consistent AOI, i.e., pupil, whole cornea, inferior half, superior half, or selected corneal region, can be selected for OST extraction and analysis over time. Experimental results show that the system can track and analyse OST change over time.

Seasonal Phragmites australis classification in Long Point National Wildlife Area wetlands using a remotely piloted aircraft system and random forest machine learning

This study produced a high-accuracy remotely piloted aircraft system (RPAS) imagery classification method for identifying the invasive reed Phragmites australis ( Cav.) Trin. Ex Steud subsp. australis using random forest (RF) machine learning. RPAS imagery was collected in the spring and fall of 2019 using a fixed-wing RPAS equipped with a visible spectrum camera (eBee X, S.O.D.A. 3D; senseFly) in Long Point, Ontario, Canada. Imagery was used to produce separate early and late season classifications and a bi-temporal classification which used imagery from both dates. The overall accuracy achieved for each was 97%, 96%, and 91%, respectively. Digital surface models (DSMs) were the most important variable for identifying Phragmites in all classifications due to their greater height when compared to surrounding herbaceous vegetation. The bi-temporal classification, which utilized change in DSM value during the growing season, resulted in an estimated 47.8% new growth of Phragmites and appeared to capture sparse growth better than traditional classification differencing alone. This study highlights the promising use of high-resolution DSMs produced from RPAS imagery to classify invasive Phragmites and monitor within-year patch expansions.

Role of Quantum Dots and Nanostructures in Photovoltaic Energy Conversion

Nanostructures and quantum dots have substantial effects on enhancing photovoltaic energy conversion efficiency, as evidenced in this comprehensive study. Materials that are nanostructured and nanosized particles are commonly used to address the urgent issues related to energy conversion. The use of nanostructured substances to address issues with energy and natural resources has garnered a lot of interest lately. Directional nanostructures in particular show promise for the conversion, collection, and storage of energy. Due to their unique properties, such as electrical conductivity, mechanical energy, and photoluminescence, quantum dots made from carbon (CQDs) and graphene quantum dots (GQDs) have been integrated into hybrid photovoltaic-thermoelectric systems (PV-TE). It evaluates the effects of nanostructures on solar energy technologies, in particular how they can improve power conversion and light absorption in solar cells. Optical light detectors, which transform photonic energy into signals that are electrical, are among the many optoelectronic uses of CQDs that have drawn attention because they are essential components of contemporary imaging and communication systems, such as visible light cameras, machine vision, medical X-ray and near-infrared image processing, and visible light detection devices. Besides supercapacitors, the study investigates how nanostructures could play a crucial role in contributing to addressing the global energy crisis sustainably, by working as photocatalysts for hydrogen synthesis and supercapacitors.

Preliminary experimental results of tungsten wire calorimeter for CRAFT NNBI

The comprehensive Research Facility for Fusion Technology(CRAFT) has been carried out since 2019 in China and as an important component of CRAFT, a negative ion-based neutral beam injection sysgem(NNBI) (200–400 keV, 2 MW) is under construction. In order to realize online diagnosis of beam uniformity, a tungsten wirecalorimeteris developed. The tungsten wire calorimeter is characterized by double-layer tungsten wire mesh structure. During beam bombardment, the tungsten wire mesh is heated and emits light, a visible light camera is used to photograph the tungsten wire calorimeter. The beam profile is obtained by digitizing the light intensity distribution map. In this paper, through the thermal simulation anslysis of tungsten wire,the installation position of tungsten wire calorimeter are determined and the spatial resolution under different beam divergence are also analyzed.The tungsten wire calorimeter was tested on the beam source test platform, the light intensity distribution maps of the tungsten wire calorimeter under 15 keV–40 keV beam energy were obtained. The work above will accumulate experience for the operation of tungsten wire calorimeter for full scale beam source in the next stage, and provide technical means for the conditioning of negative beam sources.

Characterizing Space‐Time Channel Network Dynamics in a Mediterranean Intermittent Catchment of Central Italy Combining Visual Surveys and Cameras

AbstractNon‐perennial streams have a global prevalence, but quantitative knowledge of the temporal dynamics of their flowing length—namely the extent of the wet portion of the stream network—remains limited, as the monitoring of the spatiotemporal configuration of wet channels is challenging in most settings. This work combines the high spatial resolution of visual surveys and the high temporal resolution of camera‐based approaches to reconstruct the space‐time stream network dynamics in a 3.7 km2 Mediterranean catchment of central Italy. Information on the hydrological status of the stream network derived from 40 field surveys and sub‐hourly images collected with 21 stage‐cameras are combined exploiting the hierarchical principle. The latter postulates the existence of a Bayesian chain, defined from the local persistence of the nodes that dictates their wetting/drying order during expansion/retraction cycles of the flowing stream network. Our results highlight the complexity of network dynamics in the study area: while the number of wet nodes decreases during the dry season and increases during the wet season, the local persistency exhibits a highly heterogeneous non‐monotonic spatial pattern, originating a dynamically disconnected network. Despite this heterogeneity, the hierarchical model well approximates the temporal evolution of the state of the network nodes, with an accuracy that exceeds 99%. Crucially, the model allows the reconstruction of the wet portion even in cases in which part of the network was not observed. This work provides a novel conceptual approach for the reconstruction of the wet portion of the network in poorly accessible sites.

A Real-Time Automotive Safety System Based on Advanced AI Facial Detection Algorithms

This research aims to design and implement a complete safety and comfort device that integrates a Driving Monitoring System (DMS) and a car Seat Electronic Control Unit (SECU). The DMS employs a combination of infrared sensors and a pre-trained Artificial Intelligence (AI) algorithm with advanced facial recognition capabilities. It aims to monitor and control the driver's distraction and drowsiness level during daytime and nighttime driving conditions by accurately detecting head pose and eye status. If the system detects the driver is drowsy or distracted from the road, a warning system will be activated by sending an electrical signal to the aforementioned car SECU. The designed SECU initiates a car seat vibration and triggers an audible signal to alert the driver of a dangerous situation by managing the seat movement process and protection features. It provides the basic drive and control of the seat motor drivers and position sensors. The most important consideration of this SECU is meeting the following requirements: real-time control of various motors, fast processing speed, low power, low heat dissipation, low quiescent current, and low Electromagnetic Interference (EMI). The two systems were integrated into a single compact device that includes a night vision camera mounted on the steering column to record data without obstructing the driver's view. The system has high precision at a maximum detection rate of 45 Frames Per Second (FPS) in real-time. Furthermore, the system is robust under actual driving conditions without being affected by the time of day, facial expressions, or eye occlusions.

Research on the Innovative Development of Digital Media Enabled by Computer Visual Arts

Abstract This paper explores the integration of computer visual art with digital media, highlighting the innovative potential of computer technology in creating dynamic digital media art. Initially, we review relevant theories of computer-based visual art and examine their applications in digital media art. We then detail an immersive scene design using Web3D technology, where images are processed via a computer vision camera imaging model, transformed, and analyzed with the SIFT algorithm to extract feature points. These are integrated with the incremental SFM algorithm for three-dimensional image reconstruction in immersive environments. We validate these methods with real datasets and controlled experiments to assess user experience improvements in digital media applications. Results indicate that the SIFT algorithm’s image-matching accuracy ranges from 50% to 78%, and the incremental SFM algorithm shows an average rotation error of only 0.183°. User sentiment scores improved significantly, from 11.583 to 23.452, post-experience, demonstrating enhanced realism and a new direction for digital media art.

Exploration of Virtual Reality Technology Enabling Diversified Teaching Models of Civics and Politics

Abstract The wide application of virtual reality technology has brought opportunities for teaching ideology and politics, breaking traditional education’s time and space limitations. This paper proposes an automatic three-dimensional modeling technology model, combined with computer vision camera imaging principles, to optimize the amount of data and improve optimization efficiency. And on this basis, it further proposes the Civics virtual scene construction technology, combined with the improved QEM grid simplification algorithm of edge segmentation, to optimize the virtual scene by simplifying the complex three-dimensional grid model. The experimental group’s students have shown good acceptance of this teaching method when teaching Civics in VR venues. In the cognitive and knowledge level of Civics learning, there is a significant difference between the experimental group and the control group in the dimensions of value identity and knowledge retention (P<0.05), and the mean value in the dimension of value identity is as high as 4.55, which is a very significant difference from the control class (P<0.01). And in the experiential feedback of Civics learning, the students in the experimental group have a greater sense of scene realism than the students in the control group (P<0.01), a stronger sense of realism of the interactive experience in learning (P<0.05), a higher degree of concentration in learning (P<0.01), and a more minor degree of interference by the external environment (P<0.01).

Semi-Supervised Learning With Heterogeneous Distribution Consistency for Visible Infrared Person Re-Identification.

Visible infrared person re-identification (VI-ReID) exposes considerable challenges because of the modality gaps between the person images captured by daytime visible cameras and nighttime infrared cameras. Several fully-supervised VI-ReID methods have improved the performance with extensive labeled heterogeneous images. However, the identity of the person is difficult to obtain in real-world situations, especially at night. Limited known identities and large modality discrepancies impede the effectiveness of the model to a great extent. In this paper, we propose a novel Semi-Supervised Learning framework with Heterogeneous Distribution Consistency (HDC-SSL) for VI-ReID. Specifically, through investigating the confidence distribution of heterogeneous images, we introduce a Gaussian Mixture Model-based Pseudo Labeling (GMM-PL) method, which adaptively adjusts different thresholds for each modality to label the identity. Moreover, to facilitate the representation learning of unutilized data whose prediction is lower than the threshold, Modality Consistency Regularization (MCR) is proposed to ensure the prediction consistency of the cross-modality pedestrian images and handle the modality variance. Extensive experiments with different label settings on two VI-ReID datasets demonstrate the effectiveness of our method. Particularly, HDC-SSL achieves competitive performance with state-of-the-art fully-supervised VI-ReID methods on RegDB dataset with only 1 visible label and 1 infrared label per class.

2013–2022年福建漳江口互花米草分布无人机遥感数据集

The invasion of exotic Spartina alterniflora into Chinese coastal wetlands poses a threat to biodiversity and ecosystem functions. Thus monitoring the spatio-temporal distribution of the invasion is important for the conservation/restoration and scientific management of coastal wetlands. This dataset includes S. alterniflora spatial data, derived from UAV-based RGB photos over Zhangjiang Estuary, Fujian Province from 2013 to 2022. Using a drone equipped with a visible light camera, we selected low-tide periods during the day, we captured the aerial images in the Zhangjiang Estuary wetland in Fujian Province from 2013 to 2022. Subsequently, we employed the dynamic structure-from-motion (SFM) techniques to reconstruct a three-dimensional model by mosaicking the collected aerial images to obtain a digital orthophoto image covering the study area. This process was repeated annually, resulting in a total of ten orthophoto images. Based on the classified data derived from these orthophoto images, the spatial distribution data of S. alterniflora over ten consecutive years were extracted. The high-quality UAV photos in this dataset are comprehensive and uniformly color-balanced. Located within the Zhangjiang Estuary Mangrove National Nature Reserve with coastal wetland vegetations such as mangroves and S. alterniflora, the study area is one of the most typical mangrove-S. alterniflora ecotones in China. This long-term (over ten consecutive years) and high-resolution (20 cm) image dataset for mangrove-S. alterniflora ecotone can be used in many studies including monitoring coastal wetland vegetation succession, mangrove conservation and restoration, and S. alterniflora invasion.

Edge Detection and Defects Checking of Binder Clip and Welded Joint using a Python-Based Algorithm: Applications in Quality Inspection

Machine vision is a computer vision system that enables a computer to work on image-based inspection and analysis for different applications. In this computer vision, a camera and sensor were used to view an image for its analysis with the help of some sort of algorithms, then processed to infer the image-based data. Machine vision systems along with Python programs can be used for many interdisciplinary applications like weld inspection, online monitoring in manufacturing auto components etc. In this study, the “Edge detection python algorithm” was developed and run through “Google Colab” notebook to inspect the edges and the boundaries of samples like faying surface-modified friction welded dissimilar joints and a binder clip (paper clamp) to check any defects or cracks and straightness etc. With the help of this Python algorithm, the edge detection was done by Sobel, Scharr, and Prewit operators. An input image of the weld joint and the binder clip were converted into Otsu’s binary threshold image. The matrix vision camera and the CMOS sensor were used in the machine vision set-up to take the images. This written algorithm is helpful to trace the edges of any kind of solids components. The edges of the binder clips and the weld joint/zone were detected. The binder clips were inspected under two different cases namely the clip in folding condition (Case I) and the binder clip in unfolding condition (Case II). The results showed a defect that was identified in the weld zone and no bending was in the binder clips. This kind of study is useful in manufacturing industries for quality inspection purposes with a new machine vision set up for online inspection of fabricated components like nuts and bolts etc.

Student Scientific Club in Research for Road Safety

Aim: Presentation of the potential of the student scientific club for road safety in Poland. Discuss the extent to which the student scientific club in its research activities supports and implements the concepts of identifying road safety risks and submits proposals for improving its condition and related processes. Project and methods: In the research on the visibility of vulnerable traffic participants, a theoretical method – NUR threat analysis – was used to define the research problem.A research problem was formulated in the form of a question: to what extent and under what conditions does NUR visibility affect their safety? Visibility tests were conducted in real conditions after dusk. The following recording devices were used to study the movement parameters of modern means of transportation (bicycles and electric scooters): a Gopro camera, a Sony IV RX10 camera (which realizes 4D recording), a drone with a vision camera, and a Brake Tester retarder. Results: The study of the visibility of vulnerable traffic participants from the perspective of the driver presents parameters for the visibility of pedestrians and cyclists in conditions after dusk. Participants (students) in the visibility study were made aware of the issue of visibility, and therefore the safety of the traffic participant in the situation of illuminated vehicle (bicycle) and without lighting, as well as a pedestrian on the road without a reflective element. They formulated the conclusion that seeing does not mean being seen.The result of the single-track motion tests was that the Brake Tester Model LWS -2/MC retarder, after the performance test, does not provide a measurement of the single-track motion parameters. This is an important practical finding from the research, as vehicle inspection stations are equipped with this device. The acceleration and deceleration values of the bicycle and electric scooter were determined Conclusions: The research activities of the students of the scientific club in question not only make them aware of the risk factors of becoming a victim of an accident as a vulnerable traffic participant, but also allow them to deepen their knowledge, broaden their cognitive perspectives and supplement their skills through practical activities, for example, in the area of NUR visibility determinants or the characteristics of powered vehicles. The transfer of the results of the conducted research is carried out through the participation of students in seminars and scientific conferences, as well as in journalistic activities. Keywords: scientific club, vulnerable road user, education

High-sampled structural displacement estimation through the FIR filter-based two-stage fusion of high-sampled acceleration and temporally aliased low-sampled displacement measurements

This paper proposes an FIR filter-based two-stage fusion technique for high-sampled (HS) structural displacement estimation using HS acceleration and temporally aliased low-sampled (TLS) displacement measurements. First, the temporally aliased error in the TLS displacement measurement is estimated using the acceleration measurement and then eliminated to obtain an anti-aliased low-sampled (ALS) displacement. Next, a low-frequency displacement is estimated from the ALS displacement, and a high-frequency displacement is estimated from the HS acceleration measurement. Finally, the HS displacement is estimated by combining the estimated low- and high-frequency displacements. The proposed technique is also applied to estimate the HS structural displacement by fusing a vision camera and an accelerometer. An automated algorithm is proposed to estimate a scale factor for converting a translation in a pixel unit to a displacement in a length unit and to align measurements of two sensors using short-period HS acceleration and TLS vision measurements. The performance of the proposed technique was numerically and experimentally validated. A significant improvement in the displacement estimation accuracy was achieved compared to existing FIR filter-based techniques owing to the explicit elimination of the temporally aliased error.

Heat-vision based drone surveillance augmented by deep learning for critical industrial monitoring

This study examines the application of drone-assisted infrared (IR) imaging with vision grayscale imaging and deep learning for enhanced abnormal detection in nuclear power plants. A scaled model, replicating the modern pressurized water reactor, facilitated the data collection for normal and abnormal conditions. A drone, equipped with dual vision and IR cameras, captured detailed operational imagery, crucial for detecting subtle anomalies within the plant's primary systems. Deep learning algorithms were deployed to interpret these images, aiming to identify component abnormals not easily discernible by traditional monitoring. The object detection model was trained to classify normal and abnormal component states within the facility, marked by color-coded bounding boxes for clarity. Models like YOLO and Mask R-CNN were evaluated for their precision in anomaly detection. Results indicated that the YOLO v8m model was particularly effective, showcasing high accuracy in both detecting and adapting to system anomalies, as validated by high mAP scores. The integration of drone technology with IR imaging and deep learning illustrates a significant stride toward automating abnormal detection in complex industrial environments, enhancing operational safety and efficiency. This approach has the potential to revolutionize real-time monitoring in safety–critical settings by providing a comprehensive, automated solution to abnormal detection.