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共 128 条记录，以下是91-100 订阅

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Research on ancient pottery image restoration and feature enhancement techniques based on chunked PCA and SMACC 24

Research on ancient pottery image restoration and feature en...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Tang, Bin Luo, Xiling Fan, Wenqi He, Yulong Zhao, Mingfu Chongqing university of technology Chongqing400054 China Chongqing Key Laboratory of Fiber Optic Sensing and Photoelectric Detection Chongqing400054 China Key Scientific Research Base of Pest and Mold Control of Heritage Collection State Administration of Cultural Heritage Chongqing China Three Gorges Museum Chongqing400060 China

ISBN: (纸本)9798400716942

Colored pottery has deep historical and cultural significance and value. As the pottery was buried in the tomb for a long time, it made the surface of the sample pottery attached with a lot of soil and dust, and even peeled off in some areas. In short, the pattern information on the surface of the sample pottery becomes very vague. Therefore, a method of virtual image restoration and linear feature enhancement combining the mutual fusion of chunked principal component analysis (PCA) and Sequential maximum angle convex cone analysis (SMACC) is proposed. Firstly the acquired hyperspectral images are preprocessed for easier processing and analysis, then for the preprocessed images we use Support Vector Machine (SVM) to obtain the labeling data as well as the extraction of the edges. After that the abundance map of the lines is obtained using Sequential maximum angle convex cone analysis. Finally, the obtained first principal component image and the obtained abundance map are fused to obtain the feature fusion image. In this paper, the Han Dynasty pottery lids unearthed in the Three Gorges archaeology are taken as the research object to be studied. Based on the above research, the virtual restoration results of the cloud pattern area are obtained. The research results can be used as a reference basis for ancient pottery color visual painting restoration, which is highly practical. © 2024 ACM.

关键词： image reconstruction

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819708918

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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Obstacle detection visual slam on improved YOLOv7 for complex environment 24

Obstacle detection visual slam on improved YOLOv7 for comple...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Yan, Huangbin Zhou, Xin Yin, Zhijian Yang, Zhen The College of Information Mechanical and Electrical Engineering Jiangxi Science and Technology Normal University China

ISBN: (纸本)9798400716942

Simultaneous Localization and Mapping (SLAM) is one of the fundamental functionalities for intelligent mobile robots. Over the last few decades, numerous impressive SLAM systems have been developed, demonstrating commendable performance in specific scenarios. However, some challenges still remain unsolved, such as how to handle moving objects in complex environments and how to enable robots to gain a true understanding of their surroundings and perform advanced tasks. This paper proposes a visual SLAM system, YOLOv7-SLAM, designed for complex environments with strong robustness. YOLOv7-SLAM employs a multi-threaded approach with five parallel threads: tracking, semantic segmentation, local mapping, loop closure, and dense semantic mapping creation. YOLOv7-SLAM employs the YOLO object detection method to remove obstacles in complex scenes, reducing the impact of obstacles in the environment and significantly enhancing localization accuracy in complex environments. We conducted experiments using the TUM RGB-D dataset. The results show that YOLOv7-SLAM achieves an order of magnitude improvement in absolute trajectory accuracy compared to ORB-SLAM2, making it one of the most advanced SLAM systems for complex environments. Our experiments were validated using the TUM RGB-D dataset. The results showcase a notable enhancement in the absolute trajectory accuracy of YOLOv7-SLAM in comparison to ORB-SLAM2 [1]. It performs well in complex obstacle-rich environments. © 2024 ACM.

关键词： Semantic Segmentation

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Ball detection Algorithms Enhancement in Sport Robots

Ball Detection Algorithms Enhancement in Sport Robots

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Iranian Machine Vision and image processing (MVIP)

作者： Mohammad Esfandiarpour Seyed Mani Mirshabani Ehsan Maani Miandoab Department of Engineering Science University of Tehran Tehran Iran

This paper presents a comparative analysis of four different ball detection algorithms that can be utilized in a wide range of sports robots. The study compares the performance of these algorithms in terms of accuracy, robustness, noise sensitivity, and computational time consumption. Studied algorithms are Color detection, Hough Circle, Frame differencing, and YOLOv8. Additionally, to overcome the algorithms’ drawbacks, two different combinations of algorithms are proposed. The first approach is a combination of color detection and Hough circle, and the other one is a combination of color detection and YOLO. Then, these two hybrid methods are compared to previous algorithms and it is revealed that proposed algorithms are more accurate and efficient than well-known object detection algorithms. Employing these algorithms will lead to more accuracy in sports robots.

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Multi-target tracking and detection on sea surface based on YOLOv5

Multi-target tracking and detection on sea surface based on ...

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image processing and Media Computing (ICIPMC), international conference on

作者： Jiaqi Zhu Tao Zhang Jiangnan University Wuxi China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

As a large oceanic country, our country has abundant marine resources. The detection of marine objects plays an increasingly important role in marine economic activities. Due to the frequent incidents of sea area disputes in recent years, it is of far-reaching practical significance to improve my country's sea surface monitoring technology near the sea. Maritime target detection and tracking technology will play a huge role in the field of sea surface monitoring. This paper will take ships at sea as the main research object, and study the method of multi-target monitoring and tracking at sea based on deep learning. By improving the YOLOv5 algorithm, integrating multiple attention mechanisms, and matching the DeepSORT tracking algorithm, real-time detection and tracking of maritime targets is realized. Not only that but to achieve better detection results, this paper manually created a data set of maritime targets. Through the collection of data images such as ships, buoys, islands, and reefs, a rich data set of maritime targets has been established, which can be used for better training with algorithms.

关键词： YOLO Training Sea surface Target tracking Attention mechanisms Target recognition Media

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A Study of Comparison Between YOLOv5 and YOLOv7 for detection of Cracks in Concrete Structures 3rd

A Study of Comparison Between YOLOv5 and YOLOv7 for Detectio...

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3rd international conference on Computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Anoop, Ajay Babu, Jeetu S. Department of Mechanical Engineering Amrita Vishwa Vidyapeetham Amritapuri Vallikavu India

ISBN: (纸本)9789819966899

Mechanical failures of man-made structures pose risk to human life and property. Location and mitigation of cracks before it propagates through critical regions of a mechanical/civil structure prevents the occurrence of accidents and loss of life. Crack propagation, also known as sub-critical crack propagation or stress corrosion, frequently happens under low stress and is characterized by gradual propagation. By releasing the elastic strain energy brought on by an external load creates the formation of new surfaces. Cracks spread to reduce the energy of the system. Surface cracks can be found using various non-destructive testing methods: Visual Optical Testing, Eddy Current Testing, Liquid Penetrant Testing, and Magnetic Particle Testing. This study is currently limited to the use of visual testing using computer vision, feature extraction from captured data using multiple image processing algorithms to identify cracks using an object detector model build using data points collected from a user data set. In a systematic manner, we tried to develop object detector models separately using YOLOv5 and YOLOv7 and performed a study on different standard evaluation metrics obtained from the two frameworks. YOLOv5 and YOLOv7 are the two recent additions to the YOLO family. YOLO-based object detector uses deep learning to train models for object detection using pre-trained weights from COCO data set. Results indicate further application of the detector model to be assertive on any physical structures pertaining to risk of surface cracks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： object detection

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A Deep Neural Network for image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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End-to-End 3D object detection Using LiDAR Point Cloud

End-to-End 3D Object Detection Using LiDAR Point Cloud

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Computing and Machine Intelligence (ICMI), international conference on

作者： Gaurav Raut Advait Patole University of Maryland

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Significant progress has been made in the field of au-tonomous vehicles. object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches, we have implemented a novel model intending to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in the form of 3D bounding boxes and labels of scene objects.

关键词： Point cloud compression Three-dimensional displays Laser radar Computational modeling object detection Feature extraction Encoding

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Event-based detection and tracking

Event-based Detection and Tracking

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Mi Liu Ping Xiao School of Electronic & Information Engineering Guangzhou City University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Due to the advantage of low latency, low power consumption, and high dynamic range of the event camera, it is not necessary to consider the blurring phenomenon of traditional cameras when doing target detection and tracking of moving objects. Therefore, the event camera-based multi-target tracking method generally results in better response time and accuracy[17] This report presents an object-tracking framework based on event count images. The framework contains a YOLO-based detector and multi-threaded single-object trackers based on the Kalman filter. In order to get an event count image dataset for YOLO model training and testing, a proposed event simulator is used to convert a traditional RGB image into a simulated event count image. The converted event image dataset will be fed into the YOLO model for training to get the well-trained detection model. The bounding box data extracted from each video frame by the detector serves as the initial state for the Kalman filter, subsequently enhancing the processing speed through the utilization of Python Multiprocessing. The results obtained by this tracker are used as a benchmark to compare with the results obtained by other two different trackers. The first one uses a Particle filter instead of a Kalman filter for tracking. The second one uses a tracker based on the Kalman filter and the Hungarian algorithm for tracking. The results show that the operating speed increase more than three times after adding the Multiprocessing in Python. The fastest method of the three is the Kalman filter with Multiprocessing in Python.

关键词： YOLO Training Solid modeling Target tracking Computational modeling Detectors Particle filters Data models Kalman filters Python

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Remote Sensing object detection Based on Improved detection Transformer

Remote Sensing Object Detection Based on Improved Detection ...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Yuanzhong Hu Po Hu Haihui Wang Yuzhuo Wang School of Computer Science and Engineering and School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches

关键词： Accuracy Target tracking Target recognition object detection Transformers Feature extraction Remote sensing Monitoring Software engineering Meteorology

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