High-definition Camera Research Articles

Assessing the sustainability of repetitive floor plan using Building Information Modelling-DIGITAL TWINS framework

Various factors, including the nature of the utilized equipment, labour expertise, availability of resources, and meteorological circumstances, might impact the efficiency of work on construction sites. As a result, site and planning engineers must continuously monitor and assess construction progress considering site conditions and the performance of used construction resources. In multi-storey buildings, repetitive floor construction plan impacts enormously construction site sustainability. To identify an optimal set of construction resources, this paper presents Building Information Modelling-DIGITAL-TWINs (BIMDTs) framework, which planners and site engineers can use to easily monitor and control workflow progress of the repetitive floor. Using a digital model, DTs replicate the actual workflow process on a building site and guarantee a consistent information flow. To guarantee information flow between the building site and the digital model, several communication technology tools could be deployed and tested (High-definition site construction camera, regular site monitoring using drones, infrared smart sensors, etc.). The initial results of this research show an important improvement in the sustainability of construction sites. Reduced formworks oil consumption, improved human resource safety, and increased site resource profitability were observed in several treated real cases.

Detection method for underwater dock joints: underwater sonar imaging based on 3D technology

In the detection of surface defects in underwater structures, traditional methods using manual diving are inefficient. Equipment such as underwater high-definition cameras and underwater laser imaging face significant signal attenuation in deep and turbid environments, and the information contained in two-dimensional sonar images is limited, making it difficult to meet accuracy requirements. To address these shortcomings, a detection method based on sonar imaging for underwater docks using three-dimensional (3D) reconstruction is proposed. This method first reduces environmental interference through preprocessing. Then, emit sound waves towards the underwater target and receive the returning signals, which are converted into digital signals. Next, perform 3D modeling and visualization. Finally, a detailed analysis of the 3D images is conducted to identify, analyze, and assess the severity and distribution patterns of defects. The experimental results show that the 3D scanning sonar imaging detection technology can effectively detect targets and accurately identify misalignment in caisson joints, meeting practical application requirements.

Research on belt deviation diagnosis of belt conveyors based on deep learning

Abstract Due to the slow detection speed, low accuracy, and small detection range of existing methods for detecting belt deviation in belt conveyors, this paper introduces an enhanced ultra-fast lane detection (UFLD) algorithm that leverages deep learning for the detection of belt deviation. Based on the UFLD algorithm, a variable step-size row anchor division method is proposed, and the simple parameter-free attention module is added to the network to enhance the network model’s focus on edge information of conveyor belts. Furthermore, improvements are made to the convolution operations in the ResNet-18 Stem and the downsampling operations in the residual modules, thereby enhancing the network’s ability to recognize the edges of conveyor belts. Based on the established experimental platform, a high-definition camera equipped with a track-type inspection robot was used to inspect the entire belt conveyor, covering the whole of the transmission line. The conveyor belt operation datasets collected under various working conditions were used to train and comparatively study the Hough Transform, DHT, YOLOv5, YOLOv8, LaneNet, SAD, and UFLD algorithms. The experimental outcomes demonstrate that the algorithm introduced in this article outperforms the other algorithms, achieving an F1-measure of 90.31%, an accuracy rate of 94.19 %, and a detection speed of 71 frames per second, meeting the real-time diagnostic needs for belt misalignment in the coal mining industry.

Current Paradigm in the Laparoscopic Management of Colo-Rectal Cancers

The advent of minimally invasive surgery has transformed colorectal cancer management, yet early adoption of laparoscopic techniques faced scepticism. Initial concerns included the learning curve, risks of port-site metastasis, oncological clearance adequacy, and lack of advanced surgical tools. Key developments in the 1990s—like the work of Moises Jacobs and Dennis Fowler—established foundational techniques in laparoscopic colorectal surgery. Despite its complexity and steep learning curve, improvements in training and technology have refined laparoscopic methods. Surgeons now benefit from specialized programs and advanced tools like high-definition cameras and energy devices, which enhance safety and precision. Early fears of port-site metastasis were addressed through technique refinements, reducing risks associated with tumour spread. Oncological safety studies, including major trials like the COST, COLOR, and COREAN, found laparoscopic techniques non-inferior to open surgery for survival and recurrence rates, with advantages in recovery and postoperative morbidity. The accumulated evidence confirms laparoscopic surgery as an effective, ontologically safe approach for.

Modernizing Traffic Management in Telangana: Integrating Technology for Enhanced Road Safety and Efficiency

Abstract: This work therefore aims to explore the trends of traffic management efforts in operation since the formation of Telangana in year 2014. The traffic problems have emerged due to the fast-growing urbanisation, and with the road network of now 32,445 km, Telangana has adopted new measures into addressing the challenges. The paper also considers both conventional and recent trends in policing, including advanced technology like high-definition cameras, challan prepared by computers, and software programmes incorporating artificial intelligence. Secondly, the work shows that the indicators of road safety have improved substantiationly with 2016 as the starting year, and accidents, fatalities, and injuries decreased by 2020.. Such problems as increased working hours, ceaseless training in new technologies, and data protection issues are still there, however, the case of Telangana demonstrates that in a situation of the technological progress, it is still possible to create a coherent and effective system of security and public confidence for developing countries’ regions. In this paper, it is agreed that multiplying of modern technologies with traditional police work has enhanced traffic management and road safety in the state

High-frequency study of megafaunal communities on whale bone, wood and carbonate in hypoxic Barkley Canyon

Organic-rich whale bones and wood falls occur widely in the deep sea and support diverse faunal communities, contributing to seafloor habitat diversity. Changes in community structure through succession on deep-sea bone/wood substrates are modulated by ecosystem engineers, i.e., bone-eating Osedax annelids, and wood-boring Xylophaga bivalves. Here, we use a comparative experimental approach and Ocean Networks Canada’s (ONC) cabled observatory in hypoxic Barkley Canyon to study, at high temporal resolution, colonization and succession on whale-bone, Douglas fir wood, and control carbonate rock over 8.3 mo. Experimental substrates were similar in size and mounted on PVC plates near the seafloor at 890 m depth and monitored by high-definition video camera for 5-min intervals every 6-12 h over a period of 8.3 mo. A broad range of seafloor and sea-surface environmental variables were also monitored at this site over the 8.3 mo to account for environmental variability and food input. Following loss of the high-definition camera, substrates were surveyed approximately annually with lower resolution ROV video for an additional 8.5 y. We find that megafaunal abundances, species diversity, and community structure varied substantially over 8.3 mo on each substrate, with markedly different patterns on whale bones due to the development of extensive white bacterial mats. A combination of seafloor and sea surface variables explained < 35% of bone/wood community variation. Bone-eating Osedax annelids failed to colonize whale bones even after 9.2 years, and boring Xylophaga bivalves colonized the wood at much lower rates than in better oxygenated deep-sea locations. Species diversity on whale-bone and wood substrates appeared to be substantially reduced due to the absence of ecosystem engineers and the low oxygen concentrations. We hypothesize that Osedax/Xylophaga colonization, bone/wood degradation, and bone/wood community development may be limited by oxygen concentrations of 0.22 - 0.33 ml.l on the NE Pacific margin, and that OMZ expansion due to climate change will reduce whale-bone and wood degradation, and the contribution of whale falls and wood falls to beta diversity, on the NE Pacific margin.

Enhanced 2D Hand Pose Estimation for Gloved Medical Applications: A Preliminary Model.

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding.

Investigation on lightweight identification method for pavement cracks

Mastering a lightweight method for the identification of pavement surface cracks plays a vital role in enhancing the efficiency and accuracy of pavement crack detection. However, the existing crack detection methods typically concentrate on improving model performance while neglecting the study of lightweight identification methods, causing inefficient crack detection. Therefore, this study aims to investigate a lightweight identification method for pavement cracks. High-definition vehicle-mounted cameras were employed to collect pavement crack images across multiple districts in Shenzhen, China, resulting in a dataset containing 20256 images. In addition, a pavement crack detection method based on the You Only Look Once version 8 (YOLO v8) model was proposed. On this basis, a knowledge distillation model enhanced by multiple teacher-assistants (KDMTA) was established to explore the impact of different teacher assistants and distillation paths on model performance. The results indicate that in the proposed KDMTA model, each level of teacher-assistant (TA) model can absorb diverse aspects of knowledge from the teacher models, thus broadening the range of insights. Subsequently, the information is imparted to lower-level TA models and the student model, expanding the perspective from which the student model acquires knowledge. Therefore, this improves the model's generalization performance and its effectiveness in target detection. The type and number of teacher assistant models, as well as the distillation path, influence the model performance. The random learning strategy enriches the combination and transfer of knowledge among teacher models, TA models, and student models, achieving lightweight identification for pavement crack. According to the results, the crack identification accuracy reaches 95.79 %, the mAP reaches 81.07 %, and the image processing speed has been improved by 79.6 %. This study enhances the efficiency and accuracy of pavement crack detection, providing methodological support for the rapid and accurate identification of pavement cracks. This study offers a novel method for the rapid and accurate identification of pavement cracks, which can assist management authorities to timely identify and repair pavement defects, reducing maintenance costs, and extending the lifespan of roads. Moreover, it can also facilitate the development of road safety management and intelligent transportation systems.

On the Inheritance of the Beauty of Film and Television Art to the Classic Modeling Form

The rapid progress of science and technology has greatly promoted the rapid development of film and television art in the display of classic modeling and beauty. The integration and application of high-definition camera technology, digital special effects, 3D and virtual reality (VR) and other technologies have given film and television works an unprecedented degree of delicacy and realism, making it possible to reproduce and innovate classic shapes. This deep integration of technology and art not only greatly broadens the performance boundaries of film and television creation, but also allows the audience to reach a new level of cognition and perception of the beauty of classic modeling forms in an immersive visual experience. As a powerful driving force for the inheritance and innovation of classic aesthetics in film and television art, technology not only retains the classic charm of history, but also stimulates unprecedented creative potential.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

Toward a revision of the bamboo corals: Part 6, Illuminating a new candelabrum genus (Octocorallia: Keratoisididae).

Observations and collections made using remotely operated vehicles (ROV) outfit with high-definition video cameras on bathyal seamounts of the North Atlantic and North Pacific have revealed a bamboo coral (Octocorallia, Keratoisididae) with consistent and recognizable colony morphology: a long unbranched "stem" from which many vertically aligned branches arise in a single plane to produce the aspect of a tall candelabrum. Additional observations encountered colonies with only 3 branches arising from the central terminal node to produce the appearance of a standing trident. Genetic analyses suggested both colony morphologies (trident and candelabrum) to be the same species at different growth stages. Herein we formally describe this taxon, Tridentisis candelabrum n. gen. n. sp., erecting a new genus to accommodate the unique and distinctive colony morphology, and discuss morphological variation observed across the documented geographic range.

CORAL-Catamaran for Underwater Exploration: Development of a Multipurpose Unmanned Surface Vessel for Environmental Studies.

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats.

The Value of Endoscopic Exposure of Round Window in Cochlear Implant via Facial Recess

Abstract Background Cochlear implantation (CI) has been considered as the best treatment in patients with severe to profound hearing loss unaidable with hearing aids. The main value of endoscope-assisted cochlear implantation is improved visibility of the RW and reduced risk of complications, especially in patients with congenital malformations. Aim of the Work The aim of the present study was to assess the value of endoscopic assisted CI surgery via facial recess approach without elevating tympanic anulus. Methods This Prospective case series study non-randomized sample was performed on a total of 50 patients with severe to profound hearing loss unaidable with hearing aids undergoing unilateral endoscopic assisted cochlear implant surgery with round window electrode insertion at Ain Shams University hospitals from April 2020 till March 2022 and approved 94/2020MD by an ethical committee before the start of the recruitment. Results 50 CI operations with endoscopic assistance were prospectively evaluated. There were 23 male (46%) and 27 female (54%) patients. The majority of the cases were children (41 cases, 82%). Of those 50 patients, 39 (78%) were prelingually hearing impaired. Four cases (8%) had various inner ear abnormalities. The standard mastoidectomy and Posterior Tympanotomy (PT) approach were used for all cases. Med-El (FLEX28TM; FLEX24TM Med-El, Innsbruck, Australia), Advanced Bionics (AB)Mid-Scala Electrode and CochlearTM (NUCLEUS® CI422 WITH SLIM STRAIGHT, NUCLEUS® CI24RE CONTOUR ADVANCETM, Sydney, Australia) implants were used. Endoscope-assisted visualization of the RW region through the PT was performed in all cases Rigid endoscopes with 0o and 30o (1.9, 2.7 and 4 mm in width, 11 and 6 cm in length) and an HD (high-definition) camera system (Karl Storz, Tuttlingen, Germany) were used for the endoscopic evaluation. The RW region was visualized and identified by inserting the endoscope through the PT without elevating the tympanic annulus. Then we switched to the microscope and drilled the RW niche (tegmen) using both hands until a circumferential view of the RW membrane is obtained. After proper exposure, the electrode was inserted through an opening in the RW membrane mostly under microscopical vision. Endoscopic identification of the RW through the PT enabled us to perform regular surgery in all cases. The current study concludes the difference between microscopic exposure and endoscopic exposure represented by Saint Tomas classification regarding round window exposure found that endoscopic exposure of round window classification is better represented by downgrading in the classification of round window exposure as type I 29(58%), type IIa 18(36%) type IIb 3 (6%) Non were type III by endoscopic exposure compared to microscopic exposure of round window is a type I 7(14%), type II 14(28%), type IIb 22(44%) and type III 7 (14%). Conclusion As evident from the current study, Endoscopy proved a great value in exposure and identification of RW in CI surgery through posterior tympanotomy approach, especially in difficult cases. Endoscopic-assisted CI through facial recess could be easily done using 1.9mm 0-degree endoscopy even in cases e narrow PT or rotated cochlea

A Forest Fire Smoke Monitoring System Based on a Lightweight Neural Network for Edge Devices

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time.

Design and development of a quadruped home surveillance robot

Quadruped home surveillance robots represent a promising advancement in home security and automation. This innovative robotic system is equipped with four-legged locomotion, allowing it to traverse various terrains within a household environment. The robot's primary function is surveillance, and it is equipped with high-definition cameras, motion sensors, and object recognition software. These sensors enable the robot to detect intruders, track their movements, and capture real-time video footage for remote monitoring. The quadruped robot's compact and agile design allows it to navigate through narrow spaces and overcome obstacles, ensuring it can patrol every corner of a home effectively. Its autonomous operation is made possible through advanced artificial intelligence algorithms, ensuring that it can detect anomalies and respond to security threats promptly. Furthermore, integrating the robot with smart home systems enables seamless communication with other connected devices and allows homeowners to control and monitor it remotely.

IoT based Smart Surveillance with Landmine Detection and Alerts

The research uses a Node MCU microcontroller and Internet of Things (IoT) technology to create a robot for handheld applications. The robot has a night vision camera that connects to the internet, allowing it to take photos and videos from a distance, even at night. It also has a metal detector to find landmines and an infrared (IR) sensor to detect obstacles and prevent accidents. These sensors immediately notify and alert users through LCD screens, messages, and buzzers. The research aims to develop a robot with increased accessibility and user friendliness by offering simple manual and operator controls for robotic inspection. The high-definition camera provides clear photos and videos for detailed analysis of any suspects, helping to make informed decisions quickly and improve situational awareness. On detecting a landmine, the robot alerts the user through the email and displays the information on the LCD screen, enhancing the safety at checkpoints. The IR sensor helps the robot avoid obstacles by alerting the user and sounding a buzzer when something is detected at a close distance. This feature allows the robot to navigate complex environments safely. Overall the robot is designed to provide a smart surveillance by offering an immediate threat response to handle the security concerns in various situations.

DivyaSat: Green Horizon Explorer

The "DivyaSat" project aims to tackle climate change and global warming by employing miniature, cost-effective instruments on a CubeSat platform. Inspired by the success of MeznSat, DivyaSat plans to utilize a shortwave infrared (SWIR) micro-spectrometer, called Argus 2000, to measure greenhouse gas concentrations in the atmosphere. Alongside the SWIR spectrometer, DivyaSat will incorporate a high-definition camera for accurate geolocation. This combination of instruments positions DivyaSat as a unique CubeSat mission capable of generating valuable data for atmospheric correction algorithms. This abstract provides an overview of DivyaSat's mission objectives, design, and potential contributions to understanding greenhouse gas concentrations in the atmosphere.

A method for extracting corneal reflection images from multiple eye images

The incident light reflected from the cornea is rich in information about the human surroundings, and these reflected rays are imaged by the camera, which can be used for research on human consciousness and gaze analysis, and produce certain help in the fields of psychology, human computer interaction and disease diagnosis. However, limited by the low corneal reflection ability, when a high-definition camera captures corneal reflecting rays, a large amount of color and texture interference from the iris can seriously contaminate the corneal reflection images, resulting in low usability and ubiquity of corneal reflection images. In this paper, we propose a corneal reflection image extraction method with multiple eye images as input. We align the iris regions of multiple eye images with the help of iris localization method, and by comparing multiple iris regions, we obtain the complementary iris regions, so that the iris interference in the corneal reflection region can be stripped completely. A large number of experiments have demonstrated that our work can effectively mitigate iris interference and effectively improve the quality of corneal reflection images.