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Tobacco Plant Disease dataset 14

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Tobacco Plant Disease Dataset

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection

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arXiv 2023年

作者： Huang, Junjia Li, Haofeng Wan, Xiang Li, Guanbin School of Computer Science and Engineering Research Institute of Sun Yat-sen University in Shenzhen Sun Yat-sen University Guangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology. It is critical to efficiently locate and identify cells with diverse morphology and distributions in digital pathological images. Most existing methods take complex intermediate representations as learning targets and rely on inflexible post-refinements while paying less attention to various cell density and fields of view. In this paper, we propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions and is trained collaboratively through two sub-networks, a global and a local network. The local branch learns to infer distorted input images of smaller scales while the global network outputs the large-scale predictions as extra supervision signals. We further introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. The AAT module works by learning to capture the transformed image regions that are more valuable for training the model. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Cells

Neural Network-Based Wind Field Reconstruction Method with Limited Measurement Points for Urban Blocks

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SSRN

SSRN 2024年

作者： Wang, Ranpeng Liu, Yi Li, Congjian Gao, Hang Wang, Gangqiao Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijng100084 China School of Safety Science Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin300450 China Institute of Big Data intelligent Management and Decision College of Management Shenzhen University Shenzhen518055 China

The rapid reconstruction of wind fields in urban blocks plays a crucial role in retrofitting zero-energy buildings, guiding the selection and placement of wind turbines. Although numerical methods and the Proper Orthogonal Decomposition (POD) method can provide relatively accurate results, their high computational cost and associated discontinuities pose challenges for acquiring high-spatial-resolution data affected by buildings. In this study, we develop a deep learning approach that can reconstruct wind fields affected by complex building layouts using a limited number of measuring points. The model is meticulously crafted with considerations given to model structure, loss function, and point selection method. Extensive analysis and comparisons with conventional designs are conducted to validate the proposed model. The results demonstrate that our model outperforms the conventional methods, achieving a mean absolute error in velocity of only 0.12 m/s over the inlet velocity range of 1 to 20 m/s, with only ten measurement points. Furthermore, a comparison with the POD method reveals that our model excels in extracting and preserving the crucial features of wind fields affected by complex building layouts, enabling higher migration capability. Overall, the proposed method delivers the potential for accurately reconstructing wind fields in urban blocks. © 2024, The Authors. All rights reserved.

关键词： Numerical methods

Urban Water Sprinkler Routing: A Multi-Depot Mixed Capacitated Arc Routing Problem Incorporating Real-Time Demands

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arXiv 2024年

作者： Yang, Hongtai Liu, Luna Han, Ke Lei, Boyi School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China School of Economics and Management Southwest Jiaotong University Chengdu611756 China

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinklers are usually planned based on drivers’ experience, which may lead low operation efficiency and could not respond to the real-time sprinkling demands. To address these issues, this study formulates the routes planning of sprinklers as a multi-depot mixed capacitated arc routing problem with real-time demands with the aim of minimizing the sprinklers’ travel distance. We develop an improved adaptive large neighborhood search (ALNS) algorithm that incorporates a tabu-list and a perturbation mechanism to solve this problem. Furthermore, a problem-specific acceleration mechanism is designed to reduce unnecessary search domains to improve the efficiency of the algorithm. Empirical experiments are conducted based on various scenarios and the results demonstrate that the proposed algorithm generates solutions that are superior or at least comparable to the solutions generated by the traditional ALNS algorithm but with significantly lower computation time. Sensitivity analysis is conducted to explore the effects of relevant parameters on the results. This study is the first to incorporate real-time FRD pollution information, gathered through multiple data sources via IoT technology, into urban sprinkling operations, extending the traditional CARP from a tactical planning to a real-time operational environment. A real-world implementation case is also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

Covariance-Based Device Activity Detection with Massive MIMO for Near-Field Correlated Channels

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arXiv 2024年

作者： Wang, Ziyue Li, Yang Liu, Ya-Feng Ma, Junjie The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

This paper studies the device activity detection problem in a massive multiple-input multiple-output (MIMO) system for near-field communications (NFC). In this system, active devices transmit their signature sequences to the base station (BS), which detects the active devices based on the received signal. In this paper, we model the near-field channels as correlated Rician fading channels and formulate the device activity detection problem as a maximum likelihood estimation (MLE) problem. Compared to the traditional uncorrelated channel model, the correlation of channels complicates both algorithm design and theoretical analysis of the MLE problem. On the algorithmic side, we propose two computationally efficient algorithms for solving the MLE problem: an exact coordinate descent (CD) algorithm and an inexact CD algorithm. The exact CD algorithm solves the one-dimensional optimization subproblem exactly using matrix eigenvalue decomposition and polynomial root-finding. By approximating the objective function appropriately, the inexact CD algorithm solves the one-dimensional optimization subproblem inexactly with lower complexity and more robust numerical performance. Additionally, we analyze the detection performance of the MLE problem under correlated channels by comparing it with the case of uncorrelated channels. The analysis shows that when the overall number of devices N is large or the signature sequence length L is small, the detection performance of MLE under correlated channels tends to be better than that under uncorrelated channels. Conversely, when N is small or L is large, MLE performs better under uncorrelated channels than under correlated ones. Simulation results demonstrate the computational efficiency of the proposed algorithms and verify the correctness of the analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection

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Affine-Consistent Transformer for Multi-Class Cell Nuclei De...

International Conference on Computer Vision (ICCV)

作者： Junjia Huang Haofeng Li Xiang Wan Guanbin Li School of Computer Science and Engineering Research Institute of Sun Yat-sen University in Shenzhen Sun Yat-sen University Guangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

关键词：

Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

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Frontiers of Information Technology & Electronic engineering 2022年第12期23卷 1814-1827页

作者： Weijun WANG Yun WANG Jun WANG Xinyun FANG Yuchen HE Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou 310018China Mechanical and Electrical Engineering Department Zhejiang Tongji Vocational College of Science and TechnologyHangzhou 311231China Suzhou Institute of Metrology Suzhou 215004China

As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification *** handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled *** samples with large values will serve as supplementary information for the training dataset. In addition,we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally,the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

关键词： Semi-supervised Active learning Ensemble learning Mixture discriminant analysis Fault classification

Identifying Flight Trajectory Patterns via a Density-Aided Hierarchical Clustering Algorithm 5th

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Identifying Flight Trajectory Patterns via a Density-Aided H...

5th China Aviation science and Technology Conference, 2021

作者： Zhang, Zhuxi Chen, Yichong Fang, Jing Zhou, Xueyang An, Yuhang Zhu, Xi National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beihang University Beijing100191 China Unit 32751 Beijing China School of Electronics and Information Engineering Beihang University Beijing100191 China Aviation Data Communication Corporation Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Research Institute for Frontier Science Beihang University Beijing100191 China

ISBN: (纸本)9789811674228

Identifying flight trajectory patterns is a vital task that helps controllers better understand the flight operation mechanism, so as to effectively recognize flight anomalies and manage traffic flow, etc. However, flight operation is sensitively affected by the weather and instant airspace regulation, making the flight trajectory pattern too intertwined to be easily distinguished. In this work, we propose a trajectory pattern identification method based on a density-aided hierarchical clustering algorithm. This method employs a weighted trajectory clustering mechanism to keep the minor trajectory patterns from being improperly "swallowed" by other large trajectory patterns. Experimental results show that the proposed method can explicitly distinguish different trajectory patterns and achieve more accurate results than existing approaches. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms