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Mapping grapevine leafroll disease epidemics from UAV images

Computers and Electronics in Agriculture 2025年 237卷

作者： Yixue Liu Jinya Su Dizhu Liu Yuyang Song Yulin Fang Baofeng Su Peng Yang College of Mechanical and Electronic Engineering Northwest A&F University 712100 Yangling China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs 712100 Yangling China Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service 712100 Yangling China Key Laboratory of Agricultural Remote Sensing (AGRIRS) Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University 210096 Nanjing China College of Enology Northwest A&F University 712100 Yangling China

Grapevine Leafroll Disease (GLD) poses a significant economic burden on the wine industry in major wine-producing regions. Conventional methods of phenotyping GLD are inefficient and delay vineyard management decisions. The emergence of affordable Unmanned Aerial Vehicles (UAV) provides unprecedented opportunities for GLD high-throughput phenotyping. However, detecting GLD-infected grapevines at the canopy level using UAV images is still a challenge due to the subtle differences between GLD canopy features and background. In this paper, we propose a GLD Detector (GLDD) for mapping GLD epidemics from UAV images. A new attention mechanism module, namely, Channel Attention with Transformers (CAT) is proposed to alleviate the difficulty of extracting high-resolution features from the elongated canopy. We redesigned YOLOv7-tiny for GLDD and conducted a series of ablation experiments to evaluate its performance. Experimental results show that GLDD outperforms YOLOv7-tiny by 3.1% and YOLOv6-tiny by 6.5%, reaching an accuracy of 88.2%. In comparison to several one-stage object detectors such as YOLOv5, YOLOX, PP-YOLOE, and YOLO-FaceV2, GLDD obtains the best detection results. Additionally, compared to convolutional-based detectors such as Faster-RCNN and transformer-based detector SwinT, GLDD performs better in speed and accuracy. Furthermore, GLD-infected grapevine distribution is also mapped by using GLDD detection results at the field scale.

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Identity Recognition Based on Face Image 4

Identity Recognition Based on Face Image

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Qin, Chen Lu, Xiaobo Zhang, Pengguo Xie, Huibin Zeng, Wenbin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Zhejiang Uniview Technologies Co. Ltd Zhejiang310018 China

This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Adaptive Multiscale Illumination-Invariant Feature Representation for Undersampled Face Recognition

arXiv

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arXiv 2020年

作者： Zhang, Yang Hu, Changhui Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on lambertian model and local near neighbor feature of the face image, applying to local region within multiple scales. Then, the illumination level is referenced to construct the high performance LEF as well realize adaptive fusion for multiple scales LEFs for the face image, performing JLEF-feature. In addition, the constrain operation is used to remove the useless high-frequency interference, disentangling useful facial feature edges and constructing AJLEF-face. Finally, the effects of the our methods and other state-of-the-art algorithms including deep learning methods are tested on Extended Yale B, CMU PIE, AR as well as our Self-build Driver database (SDB). The experimental results demonstrate that the JLEF-feature and AJLEF-face outperform other related approaches for undersampled face recognition under varying illumination. keywords: Undersampled face recognition, Singular value decomposition, Multiple scales edgemaps, Illumination-invariant feature extraction Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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Fire Detection based on Convolutional Neural Networks with Channel Attention

Fire Detection based on Convolutional Neural Networks with C...

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Chinese Automation Congress (CAC)

作者： Xiaobo Zhang Kun Qian Kaihe Jing Jianwei Yang Hai Yu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education of China Future Science & Technology Park Beijing China Global Energy Interconnection Research Institute State Grid Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.

关键词： Fires Semantics Feature extraction Annotations Image color analysis Deep learning Cameras

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A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence 12

A Driver Fatigue Recognition Algorithm Based on Spatio-Tempo...

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhang, Chen Lu, Xiaobo Huang, Zhiliang Xia, Shudong Fu, Changqing School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Beijing PKU TransInfo Technology Co. Ltd Beijing100193 China

ISBN: (纸本)9781728148526

Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark detection and head pose estimation network, fatigue recognition network. Experiments show that the algorithm has the advantages of small volume, high speed and high accuracy. © 2019 IEEE.

关键词： Face recognition

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Spherical formation flight of UAVs with bidirectional communication delays

Spherical formation flight of UAVs with bidirectional commun...

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第三十八届中国控制会议

作者： Xiang Ai Yang-Yang Chen School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper studies three-dimensional(3 D) formation flight problem of networked UAVs in non-holonomic kinematics under the bidirectional communication links with time delays, which makes an identity distinct from the literatures without communication delay. A novel Lyapunov function is built base on neighbors' delay information, which results in the algorithms for the surge velocity, pitch velocity and yaw velocity associated with neighbors' delay information. The system stability is proven by the Barbalat lemma. The numerical simulation result has been presented to support the theoretical results.

关键词： Sphere landing orbit tracking lateral formation forming communication delay

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3D Facial Landmarking with Denoising Auto-Encoder Networks

3D Facial Landmarking with Denoising Auto-Encoder Networks

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2019第十一届数字图像处理国际会议

作者： Liang Wang Shaoyan Gai Shuai Guo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Cardiff University

3D Facial landmarking plays an important role on 3D face recognition and face expression ***,the most of methods underperform when faces have occluded region such as hair,glasses or *** solve this problem,a coarseto-fine method is proposed,containing several denoising auto-encoder networks(denoted as DANs).DANs not only can recover the lost information but improve the accuracy of *** based on Bosphorus dataset show a 100% of good landmarking under 6 mm precision of mean error,which demonstrates that our algorithm achieves the state-of-theart performance.

关键词： 3D facial landmarking occlusion denoising auto-encoder network shape-index map coarse-to-fine

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Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm 4

Moving Vehicle Detection with Shadow Elimination Based on Im...

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Xia, Xue Lu, Xiaobo Cao, Yichao Xia, Shudong Fu, Changqing School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Beijing PKU Trans Info Technology Co. Ltd Beijing100193 China

Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Pixels

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Cloud Classification of Ground-based Cloud Images based on Convolutional Neural Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Tingting Zhu Liang Wei Yiren Guo College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing 210037 China Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) Ministry of Education Nanjing 210096 China

Cloud is a crucial meteorological factor, and also a key factor affecting solar radiation variables for solar energy, especially the attenuation of different cloud types on solar radiation. However, there are few researches on the cloud classification based on the ground-based cloud image in order to predict solar energy. In this paper, the ground-based cloud images were collected, and then they were classified into five classes based on colour, texture and the attenuation on the theoretical clear-sky solar radiation. The ResNet18 model was trained to classify the ground-based cloud images automatically. The experimental results show that the classification accuracy of the ResNet18 is up to 91%, which is higher than the other two convolutional neural networks (AlexNet and VGG16) and is also much higher than the traditional machine learning classifiers. This work provides a new perspective and important supporting data for solar radiation forecast.

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