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A Method of video Fire Smoke image Recognition Based on Computer Automatic Location

A Method of Video Fire Smoke Image Recognition Based on Comp...

International conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tian, Zhijia Wu, Xiaochuan Sha, Shuang Ministry of Emergency Management Shenyang Fire Research Institute Liaoning Shenyang110034 China

ISBN: (纸本)9783031782756

Fire smoke needs early detection and accurate identification, so as to protect people's lives and property, while manual control method has problems such as large time consumption, subjective misjudgment, so an efficient, accurate and real-time fire smoke image recognition method is needed. In this paper, a method of video fire smoke image recognition is proposed based on computer automatic location algorithm, computer vision and image processing technology, combined with optimized algorithms and high performance hardware. Through real-time processing and analysis of video frames, the method can quickly and accurately locate the fire smoke area, and realize real-time monitoring and alarm. After experimental testing, it is found that the accuracy of the proposed method is between 94% and 99%, maintaining a high accuracy. The method has high-speed processing capability and automatic characteristics, and can quickly analyze a large number of video image data and accurately locate the fire smoke area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital storage

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Coffee Bean Defects Automatic Classification realtime Application Adopting Deep Learning

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IEEE ACCESS 2024年 12卷 126503-126517页

作者： Thai, Hong-Danh Ko, Han-Jong Huh, Jun-Ho Natl Korea Maritime & Ocean Univ Dept Data Informat Pusan 49112 South Korea Natl Korea Maritime & Ocean Univ Dept Interdisciplinary Major Ocean Renewable Energ Pusan 49112 South Korea Korea Natl Open Univ Dept Agr Sci Seoul 03087 South Korea Natl Korea Maritime & Ocean Univ Dept Data Sci Pusan 49112 South Korea

The coffee industry contributes to the economic restructuring of many countries, often associated with a closed process from production to consumption. The green coffee bean grading standard provided by the Specialty Coffee Association (SCA) is one of the best methods for grading coffee beans. Traditionally, the assessment of quality and classification of coffee beans relies on visual examination, which demands significant time and effort and is easily inaccurate. Deep learning technology, characterized by precision, velocity, and veracity, can be adopted to empower the reduction of human labor and improve the productivity, quality, and efficiency of these tasks. Therefore, this paper aims to address these issues by implementing deep learning to classify coffee bean quality in real time by integrating the system with a cloud-based solution. First, image processing and data augmentation techniques are employed to handle the coffee bean image data. Subsequently, the model is trained using YOLOv8, a framework for object recognition, and OpenCV, an open-source image processing technology, to classify coffee beans. Finally, an application is developed for real-time video and image-streaming coffee bean recognition using React Native, NodeJS, and Python. The experimental results provide empirical evidence that our system enhances accuracy and efficiency in the tasks of classifying coffee bean quality in nine distinct varieties of coffee beans, with the time required reduced to a mere 1 to 3 seconds. Our system can be a useful solution for coffee producers, processors, and traders without relying on stationary equipment, especially in large farms or warehouses.

关键词： Deep learning Classification algorithms real-time systems Accuracy Nearest neighbor methods image processing Crops Defect detection YOLO Cloud computing Economics Coffee bean defects quality classification computer vision YOLOv8 OpenCV cloud-based application deep learning

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Enhancing Dental Bitewing Radiograph Datasets: A Preprocessing Approach for AI Detection and Diagnoses

Enhancing Dental Bitewing Radiograph Datasets: A Preprocessi...

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conference on real-time processing of image, Depth, and video Information

作者： Al Nassan, Wafaa Bonny, Talal Al-Shabi, Mohammad Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Univ Sharjah Coll Engn Sharjah U Arab Emirates

ISBN: (纸本)9781510673199;9781510673182

Background: The evolution of AI applications in dental imaging, covering caries detection, anatomical structure segmentation, and pathology identification, highlights the importance of high-quality datasets for effective detection models. This paper focuses on optimizing dataset quality for real-time AI-based dental bitewing radiograph detection. Methods: We systematically analyze preprocessing methods suitable for dental bitewing radiographs, covering image enhancement, noise reduction, and contrast adjustment. These techniques are strategically chosen to address common challenges in dental radiograph images, including variations in lighting, contrast disparities, and noise fluctuations. We employ optimized algorithms to meet real-time constraints, ensuring efficient model training and inference. Results: Our study assesses the impact of each preprocessing step on dataset quality and its influence on AI model performance. Practical recommendations are provided to empower researchers and practitioners in creating datasets optimized for dental bitewing radiograph detection tasks, aiming to improve AI model accuracy while adhering to real-time requirements. In addition, a comparative analysis is conducted, evaluating datasets enhanced using conventional methods against the ResNet18 model for the segmentation of bitewing dental images. Conclusion: This paper serves as a valuable guide for the dental imaging community, offering insights into preprocessing steps that elevate dataset quality for AI-driven dental bitewing radiograph detection. By emphasizing the relevance of real-time performance and providing a comparison with conventional enhancements on the ResNet18 model, we contribute to advancing early diagnosis and enhancing oral healthcare outcomes.

关键词： image processing real-time bitewing dental images Resnet 18

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Luojia 3-01 Satellite-real-time Intelligent Service System for Remote Sensing Science Experiment Satellite

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 8250-8257页

作者： Wang, Mi Wu, Qianyu Xiao, Jing Li, Deren Yang, Fang Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Remote Sensing Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China DFH Satellite Co Ltd Beijing 100094 Peoples R China

Luojia 3-01 is the world's first intelligent remote sensing satellite, equipped with various imaging modes including video, frame-pushing, and scan-pushing. It boasts submeter level multimode optical imaging capabilities, on-orbit intelligent processing, and real-time intra-satellite and satellite-to-ground data transmission. Throughout the satellite's design and development, we have established the real-time intelligent information service architecture for Luojia 3-01, marking a new paradigm in on-orbit processing and real-time services for intelligent remote sensing satellites. We have proposed the on-orbit real-time processing architecture of Luojia 3-01, addressing the challenges of limited computational and storage resources, particularly in on-orbit processing of vast volumes of remote sensing data, including core algorithmic bottlenecks in "correction-extraction-compression." A novel intelligent remote sensing satellite system has been developed, featuring multimode imaging, an open platform, intelligent processing, and satellite-to-ground interconnectivity, which significantly reduces the response time of remote sensing services to less than 8 min, thereby shortening the cycle fromdata acquisition to intelligent information service. This innovation spearheads a technological leap in remote sensing satellite services from data to information, from post-event to real-time, and from professional to widespread public applications, laying a solid foundation for the popularization and commercialization of China's intelligent remote sensing satellites.

关键词： image processing intelligent sensors intelligent systems Luojia 3-01

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real-time Deep Learning-Based Object Recognition in Augmented reality

Real-Time Deep Learning-Based Object Recognition in Augmente...

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conference on real-time processing of image, Depth, and video Information

作者： Egipko, V Zhdanova, M. Gapon, N. Voronin, V. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673199;9781510673182

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.

关键词： deep learning augmented reality computer vision object recognition robotic systems real-time processing

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BMT-BENCH: A BENCHMARK SPORTS DATASET FOR video GENERATION 31

BMT-BENCH: A BENCHMARK SPORTS DATASET FOR VIDEO GENERATION

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2024 International conference on image processing

作者： Shi, Ziang Xiao, Yang Yan, Da Min-Te-Sun Ku, Wei-Shinn Hui, Bo Columbia Univ New York NY 10027 USA Nankai Univ Tianjin Peoples R China Indiana Univ Bloomington IN USA Auburn Univ Auburn AL 36849 USA Natl Cent Univ Taoyuan Taiwan Univ Tulsa Tulsa OK 74104 USA

ISBN: (纸本)9798350349405;9798350349399

In recent years, there has been a growing interest among researchers and scholars in the analysis of sports activities, driven by the advancements of machine learning and the increased availability of public data. However, there remains a scarcity of comprehensive sports video datasets that possess the necessary attributes to address various research tasks effectively. We present the "Badminton Benchmark" (BMT-BENCH) to facilitate reproducible machine learning research in the sports domain. This dataset comprises high-quality, high-speed video clips collected from official badminton tournaments involving two team players. The dataset is labeled and unlabeled, catering to different research problems such as video generation and real-time object detection. we feature a baseline system mainly for video generation tasks and provide a thorough evaluation of the challenges posed by the dataset's unique nature. The dataset is publicly accessible at https://***/drive/folders/1moYDb8tp5K-VDxPJU3sTorfYE7NnwVpf?usp=sharing and the baseline system is available at https://***/ziangshi/BMT_BENCH_baseline_repo.

关键词： Benchmark Dataset Badminton Sport Dataset video Generation Object Detection

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The Design of the aeronautical satellite image and video emergent transmission system based on interference mitigation for artificial strong jamming and channel multiple fading 24

The Design of the aeronautical satellite image and video eme...

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International conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Yu, Shiyun Shi, Wei Pan, Yahan Natl Univ Def Technol Res Inst 63 Nanjing 210001 Jiangsu Peoples R China Nanjng Telecommun Technol Inst Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9798400709784

The uncertainty of time and place is the characteristics of taking place of the emergent incidents in the land or sea. The aeronautical satellite image and video emergent transmission system has the characteristics of high speed in motion and wider service coverage area in geography. Therefore, the aeronautical satellite image and video emergent transmission system has a great advantage over other communication methods in emergent real time image and video transmission and incident rescuing as well as other remote commanding. To insure the performance of the aeronautical satellite image and video emergent transmission system under the environment of artificial strong jamming and fading, we propose a new design of the aeronautical satellite image and video emergent transmission system based on interference mitigation for artificial strong jamming and channel multiple fading and give the design of such transmission system. The application of the mitigation method based on the adaptive antenna array is expected the very effective to reduce the influence of the artificial strong jamming and fading on the performance of the aeronautical satellite image and video emergent transmission system.

关键词： The security information transmission The aeronautical satellite communications Emergent image and video processing and transmission Jamming mitigations Multiple antenna Adaptive array nulling

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Road Intersection Analysis: Integrating image processing into Digital Twin Technologies

Road Intersection Analysis: Integrating Image Processing int...

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conference on real-time processing of image, Depth, and video Information

作者： Vdzquez Donaire, Francisco C. Abalo-Garcia, Alejandra Montemayor, Antonio S. Pantrigo, Juan J. Univ Rey Juan Carlos Calle Tulipan S-N Mostoles Spain

ISBN: (纸本)9781510673199;9781510673182

We present a real-time system for vehicle detection and classification in road intersections, incorporating image processing techniques. This system estimates the traffic flow at a specific point, as it is capable of recognizing the trajectories of different vehicles at an intersection, inferring whether they leave or enter the city. It is designed to be integrated into a high-fidelity digital twin, aiding in estimating environmental traffic pollutants. Since Computational Fluid Dynamics (CFD) use estimators like average or aggregate measurements, we use more accurate methods to estimate pollution. The implications of our study are significant for urban planning and traffic management. It allows for immediate decisions and informs long-term infrastructure planning by providing a deep understanding of intersection dynamics. Our research offers a comprehensive perspective on traffic analysis, introducing data-driven traffic management strategies for efficient urban mobility. The code developed for this purpose can be found in https://***/capo- urjc/TrackingSORT

关键词： deep learning digital twins traffic monitoring visual tracking

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Vectorized Angular Intra Prediction for Practical VVC Encoding

Vectorized Angular Intra Prediction for Practical VVC Encodi...

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2024 conference on Visual Communications and image processing

作者： Siivonen, Kari Sainio, Joose Gautier, Guillaume Mercat, Alexandre Vanne, Jarno Tampere Univ Ultra Video Grp Tampere Finland

ISBN: (纸本)9798331529543;9798331529550

Versatile video Coding (VVC) provides new coding tools for more efficient intra prediction but with a substantial increase in computational complexity. This paper introduces vectorized kernels for 8-bit angular intra prediction and position dependent intra prediction combination (PDPC), which are carefully optimized for all block sizes and prediction modes of VVC. The proposed kernels streamline the filtering process and utilize optimized memory access patterns. Our standalone tests show that the proposed vectorization achieves speedups of 6.68x for luma and 4.40x for chroma predictions over scalar implementations. Integrating these kernels into the practical uvg266 VVC encoder provides speedups of 1.07x in the slowest configuration and 1.68x in the fastest configuration. The reported speedups are obtained without any coding overhead, so the proposed vectorization plays an integral role in pursuing real-time VVC coding with high coding efficiency.

关键词： Versatile video Coding (VVC) intra prediction vectorization practical encoder implementation

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real-time Light Field video Focusing and GPU Accelerated Streaming

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND video TECHNOLOGY 2023年第6期95卷 703-719页

作者： Chlubna, Tomas Milet, Tomas Zemcik, Pavel Kula, Michal Brno Univ Technol Fac Informat Technol Dept Comp Graph & Multimedia Bozetechova 2 Brno 61200 Czech Republic

This paper proposes a novel solution of real-time depth range and correct focusing estimation in light field videos represented by arrays of video sequences. This solution, compared to previous approaches, offers a novel way to render high-quality synthetic views from light field videos on contemporary hardware in real-time. Only the video frames containing color information with no other attributes, such as captured depth, are needed. The drawbacks of the previous proposals such as block artifacts in the defocused parts of the scene or manual setting of the depth range are also solved in this paper. This paper describes a complete solution that solves the main memory and performance issues of light field rendering on contemporary personal computers. The whole integration of high-quality light field videos supersedes the approaches in previous works and the paper also provides measurements and experimental results. While reaching the same quality as slower state-of-the-art approaches, this method can still be used in real-time which makes it suitable for industry and real-life scenarios as an alternative to standard 3D rendering approaches.

关键词： Light field GPU image-based rendering

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