Search Results (541)

This article presents the development and proof-of-concept testing of a vision-based safety monitoring tool for operators in simulated robotic cells in RoboDK. The proposed method uses a virtual camera placed above the cell and image processing techniques to analyze the relationship between the operator and the workspace swept by the robot. In an initial stage, the robot movement is recorded as a mask of the swept space, and areas irrelevant to the process can be excluded by user-defined polygons. In the monitoring stage, the operator is identified in the video stream by HSV segmentation, after which an adjustable clearance zone is generated around the detected contour. Based on the intersections between the operator, clearance, swept space mask and the mask of the current robot movement, the application provides four discrete states: SAFE, WARNING, DANGER and COLLISION. For the experimental validation in the virtual environment, the virtual contact moment is estimated separately, while the COLLISION state is defined as the intersection between the inflated operator contour and the current robot motion mask. Therefore, in this study, COLLISION does not indicate measured physical contact, but an image-based imminent-collision condition used for early warning. The test scenario was carried out in a virtual palletizing cell, which includes an articulated arm robot, conveyors, manipulated objects and a mobile dummy acting as an operator. The obtained results support the use of the method as an applicative simulation solution for the evaluation of the early detection of risk situations. The study is limited to the virtual environment and represents a basis for future research on the development of visual monitoring systems to increase safety in collaborative and industrial robotic cells. Full article

The rate control algorithm is designed for natural video by default in video-coding standards. However, computer-generated screen content video (SCV) is very different from natural video captured by a camera, with many different statistical characteristics, such as sharp edges, thin lines, and flat area. This will lead to a difference in the focus of the human visual system (HVS) when viewing on-screen content video. Especially in various sensor data visualization applications such as intelligent display terminals, industrial monitoring and human–computer interaction interfaces, screen content video carries key information collected and reconstructed by image sensors, vision sensors and multimodal sensors. Its edge structures and local details directly affect the interpretation accuracy and application reliability of sensor information. Therefore, it is crucial to investigate perceptual rate control methods that integrate both video content characteristics and human visual perception properties, which possesses substantial theoretical and practical significance. In this paper, we propose a perceptual rate control algorithm for screen content video based on just-noticeable distortion (JND) which is established on the edge profile reconstruction with tolerable variations. First of all, target bit rate allocation for the frame level and CTU level is based on a perceptual weight which is calculated on the JND factor and reconstruction edge character. Secondly, under the constraint of the JND model, an intra rate-distortion (RD) model is established under the constraint of the JND model. The similarity between reference frames and reconstructed frames is taken as feedback in this model. Finally, the proposed rate control algorithm (JND–perceptual rate control (PRC)) is integrated to the existing rate control framework in High-Efficiency Video Coding–Screen Content Coding (HEVC-SCC) for improving the coding efficiency. The experimental results show that the proposed algorithm achieves better bit control precision than the platform, as well as improves the R-D performance of screen content video. In particular, compared with the HEVC-SCC reference software, the coding performance is improved by 3.09 dB on average, the bit rate is saved by 26.51% on average, and the average bit rate mismatch is within 1.159%. Full article

This article presents an integrated approach that combines Virtual Reality (VR), Augmented Reality (AR), and an Intelligent Virtual Assistant (IVA) to support training, on-the-job assistance, and maintenance in a textile manufacturing environment. The solution spans three systems: RioRV, a Unity-based VR platform for immersive, step-by-step procedure rehearsal, instructional videos, and simplified 3D animations; RiAR, a mobile AR application for assisted maintenance and access to real-time and historical machine data using marker-based (VuMark) identification; and Ria, a web-based IVA that delivers document-grounded answers, operational queries over a secure plant API, short-horizon forecasting, and a narrow set of guarded remote actions. The architecture prioritizes human-centered Industry 5.0 principles—safety, usability, and resilience—by enabling operators to learn procedures in VR, execute tasks with AR overlays and maintenance media at the workstation, and obtain concise, source-cited guidance via the IVA without leaving immersion. In the case study with a spinning section at RIOPELE, the convergence of VR, AR, and IVA reduced reliance on bulky manuals, shortened time-to-information for machine status, and established a feedback loop in which training and operational experience continuously enrich the knowledge base. Full article

The rapid advancement of information technology and multimedia applications has led to an increasing demand for video data processing. In particular, video fusion technology in multi-camera environments, which integrates and optimizes video data from multiple camera viewpoints, plays a crucial role in enhancing visual quality and improving the completeness of information. This technology addresses the challenge of obtaining high-quality video content in complex and dynamic environments. By improving image clarity, expanding perspective information, and enhancing scene understanding, video fusion technology has shown significant potential for a wide range of applications, attracting considerable attention from both academia and industry. Despite the existence of several review articles on video fusion, they tend to focus on isolated aspects of the technology and often lack a comprehensive, systematic overview of the field. To fill this gap, this paper provides an in-depth review of the research on video fusion technology in multi-camera scenarios. The paper covers the definition of video fusion; offers a detailed classification of key technologies, such as geometric correction and alignment, perspective fusion, spatio-temporal fusion, and multi-modal fusion; and explores its applications in diverse fields including surveillance security, virtual reality, film and television production, intelligent transportation, medical imaging, robotics, and unmanned aerial vehicles. Additionally, the paper examines the role of edge caching in video fusion, highlights the current challenges faced by the field, and discusses the potential of video fusion technology for driving innovation across multiple industries. Full article

To enable the deployment of road distress detection models on resource-constrained embedded platforms, this paper presents a compression case study based on the LRDD-YOLOv8n detector designed for real-time 1080p video input. Layer-adaptive magnitude-based pruning (LAMP) was integrated with channel-wise knowledge distillation. First, LAMP performs structured pruning adaptive global sparsity allocation to reduce parameters and FLOPs. Then, a larger teacher model (LRDD-YOLOv8s) with high structural similarity guides the pruned student to recover feature representations. Compared to the baseline LRDD-YOLOv8n (64.4% mAP@0.5, 2.02 M parameters, 5.9G FLOPs, and 55.5 ms GPU inference time on Jetson TX2), our compressed model under a 1/1.4 target compression ratio achieves a mAP@0.5 of 65.1% (an slight accuracy increment of 0.7%), while reducing parameters by 36.1% (to 1.29 M) and FLOPs by 30.5% (to 4.1 G). Deployed on the BOXER-8120AI edge platform (Jetson TX2), the optimized model achieves an average inference time of 48.3 ms per frame (a 13.0% latency reduction compared to the baseline model). In addition, a 20 FPS detection rate was sustained under the 30 FPS maximum hardware acquisition limit of the industrial camera stream. Kinematic and geometric analysis validates that this processing rate utilizes 66.7% of all physically available frames and establishes a 95.4% consecutive frame-to-frame spatial overlap at typical inspection vehicle speeds (40–60 km/h). Full article

Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. Full article

A micro-commission from Signal Film & Media, Barrow-in-Furness, initiated a two-year dialogue between artist/filmmaker Laurence Campbell and ecological artist David Haley. They started with the question, ‘How did such a small stream serve the development of such a large industrial town?’ Their eco-poetic, video/sonic exploration became a freshwater odyssey, discovering the blood of extinction (chalybeate-polluted water) through arterial tributaries, from deep time to 19th-century extraction to today. Their inquiry was informed by the Cumbria Archives, a local environmental conservationist, a poet and opera singer and passersby. The emergent art form revealed a complex set of ecological narratives. The project continues to raise questions about our relationships with the Nature–Climate–Culture Emergency, and the nuclear, defence, mining and water industries. Full article

This study proposes and implements a hierarchical communication system for inspection robots operating in practical caged broiler houses, where confined aisles, multi-tier cages, dust, humidity, and fluctuating wireless coverage impose challenges on stable remote inspection. The system uses Jetson Orin NX as the robot-side main controller, with industrial Ethernet and RS-485 supporting onboard device access, and Robot Operating System 2 (ROS 2) used for device coordination, data processing, and task scheduling. At the robot-cloud interaction level, Message Queuing Telemetry Transport (MQTT) is used for task delivery, status feedback, alarm reporting, and environmental data upload, while a real-time preview channel and sampled key-frame transmission support video monitoring and inspection-process retention. The proposed architecture organizes the control link and data link within an end-to-end closed loop and incorporates acknowledgment, buffering, reconnection, and abnormal-state handling mechanisms to improve communication availability under weak-network field conditions. Long-term field tests in a commercial caged broiler house demonstrated that the system could maintain reliable remote command execution, continuous status feedback, stable visual data transmission, and stable environmental telemetry upload during routine inspection tasks. The results indicate that the proposed communication architecture can provide practical support for remote inspection robots in smart poultry farming scenarios. Full article

The petroleum and petrochemical industry involves high-risk operations, where traditional manual supervision suffers from blind spots and incomplete coverage, while massive video data remain underutilized. This study collected 1.4 million images from high-risk operation sites and constructed a multi-mechanism hazard identification method using computer vision, integrating object detection, pose estimation, and object tracking. Spatiotemporal attention mechanisms were incorporated to enhance recognition accuracy for multi-scale and small targets. Based on violation behaviors, an algorithmic reasoning logic was designed to automatically identify key targets from complex video images. The study developed 40 video recognition algorithms for operational hazards (e.g., personnel standing under a crane boom and working at heights without a safety harness), achieving an accuracy of ≥90%. These algorithms enable real-time, intelligent identification of violation behaviors, facilitating the transformation of risk management from “human-based defense” to an integrated “human + technical + intelligent defense” model, allowing early intervention and elevating safety risk management standards. Full article

With society’s ever-growing concern for animal welfare, the equestrian industry has focused on passing and enforcing new rules to protect the main athlete, the horse. In jumping, courses go by quickly, with judges using the occurrence of conflict behaviours to assess any welfare breach. This study aimed to evaluate the overall frequency of such behaviours in Brazilian Sport Horses during elite show jumping competitions in Brazil. Conflict behaviour displays were analyzed in 120 different horse–rider pairs in jumping competitions in Brazil. All videos were observed at a slowed-down speed, with the frequency of behaviours being recorded. The observed behaviours included head shaking, tail swishing, neck hyperflexion, excessive pulling on the reins, kicking, bucking, rearing, and disobedience. All horses (100%) presented at least one type of conflict behaviour, with head shaking (100%, n = 120, IQR 3–7, range 1–18) being the most common one (p < 0.001) and tail swishing (45.83%, n = 55, IQR 1–10, range 1–29) being the second most common (p < 0.001). Most horses showed only one (41.67%, n = 50) or two (43.33%, n = 52) different types of behaviours, with few episodes of each throughout the course. Those findings were in line with studies in other disciplines, as the competition environment offers a series of challenging and stressful situations. The low levels of conflict observed in most horses indicate that the current horse welfare rules are working and must continue to be reinforced to consistently protect the horses. Full article

In a context of greenhouse-gas-reduction for climate-change mitigation, Optical Gas Imaging (OGI) is cited by US and EU regulations as a key technology for detecting methane leaks in the oil and gas industry. The paper outlines the principles of OGI, covering specificity of both high-performance cooled cameras and cost-effective thermal infrared uncooled cameras. It explains camera design, the optical-radiometric theory of contrast and sensitivity, and provides a comprehensive description of the key performance indicators (KPIs) such as NETD, NECL, and MDLR; together with parameters that influence them. These theoretical concepts are supported by measurements taken under laboratory conditions and outdoors, with wind and complex scenes. Finally, video-processing methods for visualizing gas leaks are presented, showing how they increase visual sensitivity and reduce the user’s cognitive load. Full article

The aim of this study was to examine the impact of professional experience on workflow performance and assess the potential of using behavioral patterns derived from video-based observation to improve work efficiency and safety culture in the food industry. The experiment was conducted at a meat processing plant, with 48 employees divided into four experience groups. Deviations from behavioral patterns, work cycle times, and an efficiency index that considered both speed and accuracy (calculated as a ratio combining task completion time and the average number of deviations) were analyzed. The results showed that experienced employees completed tasks the fastest (8.3 s/cycle) but made the most errors (an average of 4 deviations), while new inexperienced employees worked slower (15.6 s/cycle) but with fewer errors (an average of 1.5 deviations). New employees with previous experience achieved the highest process efficiency (EI = 0.031), demonstrating that a balance between speed and accuracy is crucial. The study is based exclusively on video observation, and no Motion Capture (MoCap) system was used. However, the potential future application of MoCap technology is discussed as a conceptual extension, particularly for enhancing training processes and enabling real-time feedback. Monitoring movements and providing real-time feedback can reduce errors, improve efficiency and support a culture of safety, in line with the principles of Industry 5.0 and sustainability in food production. Full article

To meet the demand for real-time and accurate diamond particle size measurement in industrial scenarios—where traditional image processing methods lack robustness in complex environments and existing deep learning models struggle to balance accuracy and efficiency—this paper proposes an integrated framework for dynamic segmentation and morphological analysis of diamond particles based on video streams. A fully automated data acquisition system consisting of a high-precision motion stage, an industrial camera, and an optical microscope is first constructed to capture dynamic particle images. Based on YOLOv8n-seg, a lightweight SPR-YOLOv8 instance segmentation model is then developed with three key improvements: a Large Separable Kernel Attention (LSKA) mechanism is introduced into the SPPF module to enhance feature discriminability; the RepBlock module is adopted in the neck network to improve feature fusion efficiency through structural re-parameterization; and a P2 small-object detection head is introduced while large-object detection branches are removed, enabling the model to focus on tiny, densely distributed particles. Finally, a contour-based geometric analysis method is proposed for particle size estimation based on segmentation results. Experimental results show that the proposed model achieves an mAP@0.9 of 0.861 while maintaining a low parameter count (0.97 M) and a high inference speed of 500 FPS. Compared with the conventional OpenCV-based method (CADPS), the proposed DPSCA framework reduces the mean absolute percentage error in particle size measurement by over 70%, while also demonstrating strong accuracy and stability in consecutive-frame tracking. Overall, this study provides a practical and efficient automated inspection solution for online quality control in superhard material manufacturing, and supplementary cross-scale experiments further demonstrate promising robustness on diamond particles beyond the primary 180–250 μm range. Full article

Polyurea elastomers are widely used in industry thanks to their exceptional mechanical properties. However, cold-pour systems typically require extended ambient curing times to achieve optimal performance. This study investigates whether accelerated thermal curing can replicate or exceed the mechanical properties obtained through the standard ambient cure protocol. Specimens were prepared by hand-mixing and then cured at temperatures of 50 $°\mathrm{C}$ and 70 $°\mathrm{C}$ for 1 h, 3 h and 6 $\mathrm{h}$. Selected specimens were then aged at room temperature for up to 7 d. Uniaxial tensile tests were conducted, with strain measured via a video-tracking technique. Porosity analysis was performed using cross-section micrographs. The results show that a 6 $\mathrm{h}$ cure at 50 $°\mathrm{C}$ yields mechanical properties comparable to those obtained through the standard ambient cure, while a 6 $\mathrm{h}$ cure at 70 $°\mathrm{C}$ significantly surpasses them. Post-cure aging was found to be particularly effective for specimens with a thickness of 1.5 $\mathrm{m}$$\mathrm{m}$, achieving a tensile strength of 4.7 $\mathrm{M}$$\mathrm{Pa}$ after 7 d, exceeding that declared by the manufacturer. Full article

Virtual Reality (VR) has emerged as a powerful tool for improving training strategies in advanced manufacturing through immersive experiences. Within this context, this study examines the impact of two training strategies, VR and Video-Based (VB) instructions, on system performance (execution time and human errors) in a cooperative Human–Robot Interaction (HRI) assembly task. Overall, 26 participants completed the task after receiving either VR or VB training, and a sub-sample of 6 people per group returned one month later to repeat the task, enabling an evaluation of performance over time. Objective and subjective metrics were collected, and statistical and effect size analyses were conducted to compare training effects across sessions. Results show that execution times and number of errors were comparable between VR and VB in the first real session. After one month, both groups exhibited improved performance, but VR-trained participants retained, on average, lower error rates, with a 71% reduction and the number of errors dropping to zero, and more stable error patterns, whereas VB-trained participants displayed greater variability and occasional accuracy degradation during repeated task execution. Moreover, within-group comparisons show that VR training is more effective for accuracy-critical cooperative HRI tasks. At the same time, VB remains a low-cost option for time-focused contexts, shedding light on how training modalities influence learning and forgetting in Industry 5.0. Full article