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State-of-the-Art deep learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review

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IEEE ACCESS 2024年 12卷 169824-169849页

作者： Nyakuri, Jean Pierre Nkundineza, Celestin Gatera, Omar Nkurikiyeyezu, Kizito Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

Plant pest and disease management, especially in the early stages of infestation, is a critical challenge that poses significant threats and has potential to devastate agricultural crops, causing total yield loss and food insecurity. Traditional inspection methods are time-consuming and prone to errors due to limited labor expertise. Therefore, to tackle these challenges, harnessing advanced technologies such as artificial intelligence (AI), Machine learning/deep learning (ML/DL), and Internet of Things (IoT) is essential for managing and mitigating agriculture hazards. This research presents a comprehensive review of the state-of-the-art DL architectures integrated with IoT-based systems applied to plant pest and disease detection (PPDD) by investigating different potential approaches that have been employed using DL and IoT up to the year 2024 to address challenges in agriculture. Convolutional Neural Network (CNN) architectures for image recognition, object detection, and their integration with IoT, embedded into mobile devices and unmanned aerial vehicles (UAV) are explored. Moreover, the research discusses the advantages and limitations of these techniques, emphasizing their architecture design, efficiency and accuracy. The findings demonstrate that there is a tradeoff between robustness and complexity among existing techniques, and authors recommend future trends aimed at creating robust models with fewer parameters that are more accurate and easily implementable on small IoT-based and portable devices suitable for in-field and real-time applications. Furthermore, while existing review papers discuss either DL or IoT separately, this research paper uniquely focuses on their combined models, providing a comprehensive overview of the synergistic potential of leveraging IoT-driven technologies alongside advanced DL algorithms to ease the task of researchers in the field of precision agriculture particularly in PPDD.

关键词： Crops Internet of Things Reviews Agriculture image recognition Accuracy deep learning Plant diseases Convolutional neural networks machine learning crop pest detection crop disease detection convolutional neural networks

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learning deep Scene Curve for Fast and Robust Underwater image Enhancement

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IEEE SIGNAL processing LETTERS 2024年 31卷 6-10页

作者： Xue, Xinwei Ma, Tianjiao Han, Yidong Ma, Long Liu, Risheng Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China

learning-based approaches inspired by the scattering model for enhancing underwater imagery have gained prominence. Nevertheless, these methods often suffer from time-consuming attributable to their sizable model dimensions. Moreover, they face challenges in adapting unknown scenes, primarily because the scattering model's original design was intended for atmospheric rather than marine condition. To address these obstacles, we begin by investigating the inherent differences in imaging characteristics between atmospheric and marine conditions based on statistical distributions. Building on these observations, we introduce an efficient and effective algorithm called deep Scene Curve, abbreviated as DSC. This method comprises two essential steps: scene-irrelevant zero-mean adjustment and scene-oriented hyperparameter estimation. The first step transforms scene features into a unified zero-mean space, thereby reducing interference from scene-specific attributes. In the second step, we employ a lightweight neural network to estimate scene-oriented hyperparameters for a defined pixel-level curve based on underwater observations. This approach enables us to generate a deep curve that excels in both adaptability and efficiency, as substantiated by extensive experiments. Notably, our method achieves a significant 56% improvement in average inference time while reducing FLOPs by 92% compared to existing techniques. Furthermore, our extensive experiments in low-light image enhancement tasks highlight the potential advantages of DSC.

关键词： image color analysis Training Adaptation models image enhancement Computational modeling Atmospheric modeling Estimation deep learning underwater image enhancement curve estimation

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An adversarial pedestrian detection model based on virtual fisheye image training

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SIGNAL image AND VIDEO processing 2024年第4期18卷 3527-3535页

作者： Zhang, Jindong Dou, Jian Jilin Univ Coll Comp Sci & Technol Qianjin St Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Qianjin St Changchun 130012 Jilin Peoples R China

Fisheye camera is an important sensor in on-board systems and surveillance systems. However, the current fisheye image pedestrian detection still exists problems such as large distortions are difficult to detect, sparse datasets, and poor real-time performance. We propose an adversarial pedestrian detection model based on virtual fisheye image training. The fisheye transformation model is proposed to generate virtual image dataset, which provides a complete data base for network training. The DEMO-YOLO network is used to ensure real-time performance while improving accuracy, and the spatial transformation network is introduced to convert the features extracted from the backbone network into hard examples. In the process of training, adversarial learning is used so that the network is able to deal with objects with large deformations. The model is trained on the fisheye-transformed CityPersons and Kitti datasets and tested on the real fisheye dataset Woodscape. The experimental results show that compared with the traditional detection methods, the accuracy of the proposed model is improved by 18.45% and the real-time performance reaches an average of 318 frames/s. Therefore, the model can be applied to fisheye cameras.

关键词： Fisheye image Pedestrian detection Adversarial learning DEMO-YOLO

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Computation Offloading Optimization in Satellite-Terrestrial Integrated Networks via Offline deep Reinforcement learning

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IEEE INTERNET OF THINGS JOURNAL 2024年第23期11卷 38803-38814页

作者： Xie, Bo Cui, Haixia Cao, Peng He, Yejun Guizani, Mohsen South China Normal Univ Sch Elect & Informat Engn Foshan 528225 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

As the demand for global Internet connectivity continues to grow, the satellite-terrestrial integrated networks (STINs) have become more and more crucial for expanding the service coverage and enhancing the network performance. However, the task offloading problem in STINs faces many significant challenges, such as high processing latency and energy consumption. The current intelligent offloading strategies often rely on the real-time interactions with the environments which not only consume valuable satellite resources but also cause irreversible damage to the satellite equipment due to some operational errors. To address these issues, in this article, we propose an offline deep reinforcement learning (offline DRL) approach to learn and optimize the task offloading decisions by leveraging the stored historical decision data and employing the soft actor-critic (SAC) algorithm specifically. Experimental results show that the proposed strategy outperforms most of the existing methods in terms of latency and energy consumption and effectively reduces the direct interactions with STINs.

关键词： Satellites Low earth orbit satellites Delays Energy consumption real-time systems Planetary orbits Internet of Things Offline deep reinforcement learning (offline DRL) satellite-terrestrial integrated networks (STINs) soft actor-critic (SAC) task offloading

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DRL-Based Joint Task Scheduling and Trajectory Planning Method for UAV-Assisted MEC Scenarios

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IEEE ACCESS 2024年 12卷 156224-156234页

作者： Gu, Cheng Li, Fan Liu, Dong-Sheng Wu, Yi-Xuan Wang, He-Xing Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Inner Mongolia Univ Sch Comp Sci Hohhot 010000 Peoples R China Inner Mongolia Univ Coll Software Hohhot 010000 Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Peoples R China

The proliferation of Internet of Things (IoT) devices has resulted in a massive increase in data generation, necessitating robust solutions for real-time data processing and analysis. The integration of Unmanned Aerial Vehicles (UAVs) with MEC systems presents a promising enhancement, providing dynamic, mobile edge computing capabilities that can adapt to changing conditions and demands. However, efficiently managing the task offloading and trajectory planning of UAVs in such scenarios poses significant challenges, particularly in maximizing coverage while minimizing time and energy consumption. In this context, this paper proposes a novel UAV-based MEC model utilizing the Multi-Agent deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm to optimize task scheduling and UAV trajectory in real-time. Our model enables UAVs to function as mobile edge servers, dynamically processing and routing tasks between terminal devices and edge servers based on real-time device demands. By incorporating a sophisticated reward function that prioritizes the minimization of system costs-including energy, time, and throughput maximization-our approach not only enhances the operational efficiency of UAV-assisted MEC systems but also improves the quality and responsiveness of edge computing services. After training the MADDPG model in the simulated environment, the experiments show that our approach significantly improved the performance of UAV-assisted MEC systems in real-time scenarios, converging after 50 learning episodes and achieving 90% task completion rate.

关键词： Autonomous aerial vehicles Servers Signal to noise ratio Computational modeling real-time systems Internet of Things Resource management Processor scheduling Data models Trajectory planning Mobile edge computing UAV deep deterministic policy gradient task offloading trajectory planning

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Enhancing Sample Utilization in Noise-Robust deep Metric learning With Subgroup-Based Positive-Pair Selection

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IEEE TRANSACTIONS ON image processing 2024年 33卷 6083-6097页

作者： Yu, Zhipeng Xu, Qianqian Jiang, Yangbangyan Sun, Yingfei Huang, Qingming Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods.

关键词： Metric learning noisy label deep learning deep learning pair- wise loss pair- wise loss pair- wise loss pair- wise loss positive-pair selection positive-pair selection

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Fast and customizable image formation model for optical coherence tomography

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BIOMEDICAL OPTICS EXPRESS 2024年第12期15卷 6783-6798页

作者： Mazzolani, Andrea Macdonald, Callum Munro, Peter R. T. UCL Dept Med Phys & Biomed Engn Malet PlGower St London WC1E 6BT England

Optical coherence tomography (OCT) is a technique that performs high-resolution, three-dimensional, imaging of semi-transparent scattering biological tissues. Models of OCT image formation are needed for applications such as aiding image interpretation and validating OCT signal processing techniques. Existing image formation models generally trade off between model realism and computation time. In particular, the most realistic models tend to be highly computationally demanding, which becomes a limiting factor when simulating C-scan generation. Here we present an OCT image formation model based on the first-order Born approximation that is significantly faster than existing models, whilst maintaining a high degree of realism. This model is made more powerful because it is amenable to simulation of phase sensitive OCT, thus making it applicable to scenarios where sample displacement is of interest, such as optical coherence elastography (OCE) or Doppler OCT. The low computational cost of the model also makes it suitable for creating large OCT data sets needed for training deep learning OCT signal processing models. We present details of our novel image formation model and demonstrate its accuracy and computational efficiency.

关键词： Biomedical imaging Computer simulation deep learning Optical imaging Signal processing Spectral domain optical coherence tomography

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The Application of 6G and Augmented reality Technology in Education and Training

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-19页

作者： Zhang, Yangloucai Bin Azizui, Muhammad Farid Azizul Yan, Cong Univ Teknol Malaysia Fac Built Environm & Surveying Skudai 81200 Malaysia Chengdu Neusoft Univ Sch Digital Art & Design Chengdu 61000 Peoples R China Univ Teknol Malaysia Coll Educ & Surveying Skudai 81200 Malaysia

The short evolution of 6G and augmented reality (AR) technology heralds a transformative era in education and training, promising to redefine traditional learning paradigms using immersive, interactive reports. This paper explores the integration of advanced capsule networks (CapsNet) architectures with semantic segmentation techniques to cope with the inherent annoying conditions of applying 6G and AR in academic contexts. CapsNets, well known for its efficiency in discerning spatial hierarchies and relationships, are paired with semantic segmentation to enhance object recognition and interplay within AR environments. It offers a deep, contextual understanding essential for growing dynamic, personalised learning studies. The synergy among this technology allows for the unique delineation and classification of each image pixel into corresponding object training, facilitating seamless interaction among virtual content and the real world. Need of the highly dependable, low-latency communication exchange skills of 6G, this combination guarantees powerful processing of complex scenes in real-time, crucial for preserving student engagement and facilitating adaptive learning pathways. The paper discusses optimisation techniques for real-time performance, including version pruning and effective routing mechanisms, to cope with the computational needs of CapsNets and semantic segmentation. Moreover, it highlights the role of 6G in assisting the bandwidth and latency necessities critical for deploying these state-of-the-art AI models in AR-based instructional programs. Through this integration, we aim to release new potentials for personalised, context-aware learning experiences, which can considerably enhance knowledge retention, engagement, and accessibility in education and training across various fields.

关键词： 6G Augmented reality Capsule networks Semantic segmentation Training Personalised learning

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Anomaly Detection in Logs Using deep learning

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IEEE ACCESS 2024年 12卷 176124-176135页

作者： Aziz, Ayesha Munir, Kashif Natl Univ Comp & Emerging Sci FAST Sch Comp Islamabad 44000 Pakistan Arab Open Univ Fac Comp Studies Riyadh Saudi Arabia

Detection of abnormalities is important for the security and reliability of computer systems as they heavily rely on logs to detect anomalies. The logs provide general information, errors, warnings, and debugging information. Existing approaches for detecting anomalies are sometimes inaccurate due to their limitations related to log-processing leading to loss of semantic significance. Existing approaches, like deeplog and LogAnomaly, have restrictions in detecting irregularities in log frameworks mainly in large dynamic systems. In this paper, we propose a hybrid anomaly detection technique that combines unsupervised approaches such as Self-Organizing Maps, Bert Encoders, and Autoencoders to handle these issues. The approach improves anomaly identification accuracy by employing semantic vectors obtained by the Bert Encoder to recognize patterns with autoencoders. The evaluation results show that the proposed strategy outperforms the existing methods for various types of data including system logs, network traffic, and financial transactions.

关键词： Anomaly detection deep learning Semantics Encoding Bidirectional control real-time systems Data models Pipelines Machine learning algorithms Clustering algorithms classification unsupervised learning deep learning

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Analysis of Students' Class Status Based on deep learning 20th

Analysis of Students' Class Status Based on Deep Learning

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20th Chinese Intelligent Systems Conference

作者： Liu, Qunpo Zhang, Xiaojie Wu, Hongpei Henan Polytech Univ Jiaozuo 454003 Henan Peoples R China Henan Int Joint Lab Direct Drive & Control Intell 2001 Century Ave Jiaozuo 454003 Henan Peoples R China

ISBN: (纸本)9789819786565;9789819786541;9789819786534

Aiming at the problem that it is difficult for teachers to obtain accurate classroom status in daily teaching process, which is not conducive to making targeted adjustments to teaching methods, this paper proposes a deep learning-based student class status analysis system. The system uses camera to capture classroom video, and uses image recognition, target detection, deep learning and other technologies to detect the behavioral state of students in the classroom in real time and concentration information, and through the statistical analysis of the collected data, it helps the teacher to get timely feedback in the classroom, and better judge the learning state and concentration of students. In order to realize the real-time and accuracy of the system design, the system in the paper introduces the OpenPose model into the YOLOv5 network to identify the students' skeletal keypoints, and synthesizes the results of the processing of the YOLOv5 model and the OpenPose model to make an analysis of the students' classroom behaviors and concentration. The experimental results show that the loss curve can achieve good convergence, and the AP and mAP can reach 95.1% and 88.0%, respectively.

关键词： Student status detection deep learning YOLOv5 OpenPose

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