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SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Mu, HaoDong Li, Haipeng Chen, Zhong Huazhong University of Science and Technology 1037 Luoyu Road HuBei WuHan430074 China Beijing Aerospace Automatic Control Institute Bejing100854 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

As remote sensing technology continues to advance, the accuracy and quantity of remote sensing images have significantly improved. The generation of a vast amount of available data has facilitated the widespread application of various deep learning methods in the field of remote sensing data processing, such as object detection, semantic segmentation, and change detection. In the aforementioned tasks, Change detection is used to identify alterations occurring on the Earth's surface by utilizing remote sensing (RS) data. In recent years, deep learning based methods have exhibited significantly superior performance compared to traditional change detection techniques. The fundamental strategy enabling these advancements involves extracting appropriate deep learning features from input remote sensing images through various backbone networks such as VGG, ResNet, DenseNet etc. Nevertheless, the features extracted by the aforementioned backbone networks may not fully cater to the specific requirements of remote sensing image change detection tasks. Consequently, our goal is to explore the influence of features extracted by different backbone networks on change detection tasks and introduce a specialized backbone network tailored for change detection. This endeavor aims to produce features that are better suited for the of change detection. The experimental results indicate that our specifically designed feature extraction network for remote sensing image change detection outperforms traditional networks in extracting task-specific features. These features are better suited for subsequent decoder modules, enhancing the generation of image-based change detection results. At the same time, we found that when using general backbone networks for change detection, ResNet achieves the highest metric accuracy, while DenseNet has the lowest memory usage and the fastest training and testing speed. Depending on the specific task, we can choose the appropriate backbone network as need

关键词： Feature extraction

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Forest Wildfire Detection from Satellite image Using deep learning

Forest Wildfire Detection from Satellite Image Using Deep Le...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Elizaroshan, D. Raj Kumar, J.S. Karunya Institute Of Technology And Sciences Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350352689

This study presents an improved methodology of wildfire detection in the forest using PyTorch, a wise machine learning framework, and the Flask application for real-time monitoring. Focusing on accuracy and speed in detection, the methodology mainly refers to data acquisition, and preprocessing of satellite image datasets in its essence, meanwhile featuring the training of robust models through data augmentation. The experimental results using the PyTorch model, which depends on DenseNet201, have proven its ability to determine patterns and reflect highly dynamic contrasts between vertical and horizontal pans;this is especially true when the principal element of the considered satellite image is diffuse and indeterminate. The framework has been integrated into a user-friendly Flask web app, and it gives outputs of immediate wildfire detection upon image upload. The approach is also shown to deliver better metrics thus a more efficient solution as proven by the experimental results. The study informs environmental technology about introducing an easy-to-apply, applicable, and adaptive tool that may have potential associations with environmental agencies for environmentally sustainable approaches to wildfire management. © 2024 IEEE.

关键词： Environmental technology

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Evaluating the Energy Efficiency of Few-Shot learning for Object Detection in Industrial Settings 3

Evaluating the Energy Efficiency of Few-Shot Learning for Ob...

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IEEE 3rd real-time and Intelligent Edge Computing Workshop (RAGE)

作者： Tsoumplekas, Georgios Li, Vladislav Siniosoglou, Ilias Argyriou, Vasileios Goudos, Sotirios K. Moscholios, Ioannis D. Radoglou-Grammatikis, Panagiotis Sarigiannidis, Panagiotis MetaMind Innovat PC R&D Dept Kozani Greece Kingston Univ Dept Networks & Digital Media Kingston Upon Thames Surrey England Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece Aristotle Univ Thessaloniki Phys Dept Thessaloniki Greece Univ Peloponnese Dept Informat & Telecommun Tripoli Greece K3Y Ltd Dept Res & Dev Sofia 1000 Bulgaria

ISBN: (纸本)9798350363357;9798350363340

In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection models to downstream tasks is examined. Subsequently, a thorough case study and evaluation of the energy demands of the developed models, applied in object detection benchmark datasets from volatile industrial environments, is presented. Specifically, different finetuning strategies, as well as utilization of ancillary evaluation data during training, are examined, and the trade-off between performance and efficiency is highlighted in this low-data regime. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.

关键词： Few-Shot learning Green AI deep learning Model Optimization Object Detection Industrial image Data

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Temperature Forecasting of Grain in Storage: An Improved Approach Based on Broad learning Network

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IEEE ACCESS 2024年 12卷 115112-115123页

作者： Wang, Qifu Hou, Minglei Qin, Yao Lian, Feiyu Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China Henan Acad Sci Inst Appl Phys Zhengzhou 450001 Peoples R China Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China

Temperature forecasting of grain in storage is crucial for timely granary temperature control, mitigating adverse effects of extreme temperatures on grain quality. Traditional machine learning methods struggle with stability and high error rates in grain storage temperature forecasting, while deep learning models are more accurate but time-consuming and have heavy parameters. To address these problems, an improved model with light weight and good accuracy is proposed in this paper, which broad learning network is combined with one-dimensional convolution module and multi-head self-attention mechanism (BLN-1DCNN-MHSA). Firstly, we employ a one-dimensional convolution module at the feature nodes of the model to extract local temporal correlations, compensating for temporal sequence learning limitations of the BLN. Secondly, a multi-head self-attention mechanism at the enhancement nodes to captures important features dependencies and global temporal correlations. Lastly, our model achieves better prediction through enhanced representation ability of model nodes. The results with real grain storage temperature data demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.341, 0.54%, 0.28, respectively, which represent more than 2 times improvement in accuracy compared to the BLN, and it also reduces training time by more than 90% compared with LSTM and Transformer models. Additionally, the generalization and robustness of the improved approach are demonstrated through promising results in a classification experiment on the MNIST dataset. In general, the model provides a certain feasibility for early warning of grain storage risks by predicting its temperature trends.

关键词： Predictive models Feature extraction Forecasting Accuracy Data models Temperature distribution Mathematical models Storage management Convolutional neural networks Grain storage temperature forecasting grain storage security broad learning network multi-head self-attention convolutional neural network

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A Robust Large-scale Dataset for Assessing Light Field image Quality

A Robust Large-scale Dataset for Assessing Light Field Image...

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9th International Conference on Signal and image processing (ICSIP)

作者： Ma, Jian Liu, Zhuochi Xu, Yaozong Yu, Jiajun Wang, Yang Liu, Yuhao Anhui Univ Sch Internet Hefei Peoples R China

ISBN: (纸本)9798350350920

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. deep learning methods, leveraging neural networks, have shown promising performance in image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field image Quality, named RLSD, specifically designed for evaluating the quality of light field images. The dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. The dataset is publicly available at the following link: "https://***/s/1kJmx4qsy8ywLPba-HwGCEg" (password: XY28).

关键词： Light field image quality assessment RLSD distortion Single stimulus continuous quality evaluation

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Enhancing Gemstone Classification Accuracy: A deep learning Approach Using CNN, VGG-19, and Xception 1

Enhancing Gemstone Classification Accuracy: A Deep Learning ...

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Nadimpalli, Mani Salya Kumar Baleboyina, Karthik Madhu Kiran Vundavalli, Yasaswi Durga Tummalapenta, Tej Tarun Kamepalli, Sujatha Vfstr Deemed to be University School of Computing Guntur India

ISBN: (纸本)9798350376517

Gemstones demand has been steadily on the rise in recent years, driven by various factors. Notably, the soaring popularity of colored gemstones in jewelry designs and the pervasive influence of social media platforms significantly contribute to this trend. A key development in the gemstone market is the growing consumer preference for ethically sourced stones. As consumers become more socially aware, they actively seek out gemstones that are mined and processed with a strong commitment to environmental sustainability and ethical practices. Some gemstones may look alike but their properties, price, rarity may vary, so wrong certification or recognition of the gemstone can be a problem for both dealers and customers. To avoid this there are pre built models like CNN to classify images automatically. In this paper traditional CNN, VGG-19, Xception models are implemented and trained on a large gemstone dataset containing 87 classes. The main aim is to increase the accuracy of the models which can become potential source to classify a gemstone in real time. Finally, it was proved that Xception network is best suited for this task with an accuracy about 85.21 percentage. This marks the rise of deep learning models in the gemstones field © 2024 IEEE.

关键词： CNN deep learning Models Gemstones image Augmentation VGG-19 Xception

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SSLCT: A Convolutional Transformer for Synthetic Speech Localization 7

SSLCT: A Convolutional Transformer for Synthetic Speech Loca...

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7th IEEE International Conference on Multimedia Information processing and Retrieval (MIPR)

作者： Bhagtani, Kratika Yadav, Amit Kumar Singh Bestagini, Paolo Delp, Edward J. Purdue Univ Video & Image Proc Lab VIPER Sch Elect & Comp Engn W Lafayette IN 47907 USA Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9798350351439;9798350351422

deep learning methods can now generate high quality synthetic speech which is perceptually indistinguishable from real speech. As synthetic speech can be used for nefarious purposes, speech forensics methods to detect fully synthetic speech have been developed. Speech editing tools can also create partially synthetic speech in which only a part of the speech signal is synthetic. Detecting these short synthetic segments within a speech signal requires specialized methods to determine the temporal location of the synthetic speech. In this paper, we propose the Synthetic Speech Localization Convolutional Transformer (SSLCT), a neural network and transformer method for synthetic speech localization. SSLCT can temporally localize synthetic speech segments as small as 20 milliseconds. We demonstrate that SSLCT achieves less than 10% Equal Error Rate (EER), which is an improvement over several existing methods.

关键词： Synthetic speech localization speech forensics deepfake speech PartialSpoof transformer

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Road Surface Crack Detection Based on Improved YOLOv9 image processing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 590-600页

作者： Li, Quanwu Duan, Shaopeng Zhengzhou Vocat Univ Informat & Technol Sch Informat Engn & Artificial Intelligence Zhengzhou 450008 Peoples R China

Road surface crack detection is a critical task in road maintenance and safety management. Cracks in road surfaces are often the early indicators of larger structural issues, and if not detected and repaired in time, they can lead to more severe deterioration and increased maintenance costs. Effective and timely crack detection is essential to prolong road lifespan and ensure the safety of road users. This paper introduces CrackNet, an advanced crack detection model built upon the YOLOv9 architecture, which integrates a fusion attention module and task space disentanglement to enhance the accuracy and efficiency of road surface crack detection. Traditional methods often struggle with the complex and irregular nature of road cracks, as well as the challenge of distinguishing cracks from their backgrounds. CrackNet overcomes these challenges by leveraging an attention mechanism that highlights relevant features in both the channel and spatial dimensions while separating the tasks of classification and regression. This approach significantly reduces false negatives and improves localization accuracy. The effectiveness of CrackNet is validated through comparative analysis with other segmentation models, including Unet, SOLO v2, Mask R-CNN, and deeplab v3+. CrackNet consistently outperforms these models in terms of F1 and Jaccard coefficients. This study highlights the critical role of accurate crack detection in minimizing maintenance costs and enhancing road safety.

关键词： Road crack YOLOv9 deep learning surveillance

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Defect Detection Model Using time Series Data Augmentation and Transformation

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Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Human-Aware Dynamic Hierarchical Network Control for Distributed Metaverse Services

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第3期42卷 629-642页

作者： Chen, Qimei Li, Ruixue Xu, Xiaoxia Wu, Jing Jiang, Hao Qiu, Meikang Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

Metaverse has emerged as a revolutionary technique for transforming the way people interact with digital content, which relies on a distributed computing and communication infrastructure, encompassing terminal users, edge servers, and cloud servers. However, the rapid evolution of the Metaverse presents challenges that surpass the capabilities of existing communication and network infrastructures, particularly on network bandwidth and latency. Additionally, human experience becomes a critical factor in this domain. Therefore, we introduce a human-aware hierarchical software defined network (SDN) architecture consisting of a Metaverse cloud layer, a mobile edge computing (MEC) server empowered edge layer, and a distributed terminal layer. Each MEC server dynamically controls a multi-antenna base station (BS) and several reconfigurable intelligent surfaces (RISs) according to the terminal immersive experience requirements in real-time. To overcome the bandwidth limitation, we propose a novel smart reconfigurable spatial reuse new radio in unlicensed spectrum (NR-U) framework, which can realize customizable communications through flexibly and coordinately reconfiguring beams among the coordination between BSs and RISs. The objective function is formulated as a Lyapunov optimization based decentralized partially-observable Markov decision process (Dec-POMDP) problem to maximize the spectral efficiency while guaranteeing the latency and reliability requirements in Metaverse, via a joint user selection, phase-shift control, and beam coordination strategy. To solve the above non-convex, strongly coupled, and mixed integer nonlinear programming (MINLP), we propose a novel multi-agent hierarchical deep reinforcement learning (MAHDRL) algorithm that integrates deep Q-network (DQN) to solve discrete problems, deep deterministic policy gradient (DDPG) to solve continuous problems, and mixing network to capture complex interactions between multiple agents. Numerical results demo

关键词： Metaverse Servers Computer architecture Wireless fidelity Bandwidth Array signal processing Interference Human-aware dynamic network control multi-agent distributed coordination NR-U RIS metaverse

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