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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Fine-Tuning-Powered Plant Disease Detection Based on Cloud-Edge Computing in Smart Farms

Fine-Tuning-Powered Plant Disease Detection Based on Cloud-E...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Da Meng Wanxing Jiang Wenquan Jin Department of Electronic & Communication Engineering Yanbian University Yanji China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Plants are an indispensable part of agricultural production, and the study of plant disease classification and recognition has an important impact on garden horticulture, environmental protection, crop growth and the continuous development of agricultural economy. Moreover, it plays a very important role in crop production intelligence and crop big data management applications. Traditional machine learning relies too much on expert knowledge, and the form of artificial extraction of plant leaf information cannot meet the recognition of complex diseases. In this paper, we come up with an edge-cloud plant disease classification approach by using fine-tuning to improve the performance continuously. Methods of crop disease identification leverages convolutional neural networks (CNN) that achieves successful performance in various computer vision tasks. The improved leaf recognition model performs the collection, analysis, processing and prediction of heterogeneous data in the process of crop planting. The proposed fine-tunning-improved CNN model is used for searching three common potato diseases in real time. In the cloud, the dataset is deployed to derive the prediction model using CNN for performing fine-tuning in the edge device. Based on the presented approach, the performance of the model is improved and the time consuming decreased by fine-tuning method in the edge environment.

关键词： Performance evaluation Plant diseases Computational modeling image edge detection Crops Production Predictive models Data models Convolutional neural networks Tuning

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

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Proceedings of the Digital image Computing: Technqiues and applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Deep defense: training DNNs with improved adversarial robustness 18

Deep defense: training DNNs with improved adversarial robust...

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Proceedings of the 32nd International conference on neural Information processing Systems

作者： Ziang Yan Yiwen Guo Changshui Zhang Institute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology (BNRist) Department of Automation Tsinghua University Beijing China Intel Labs China and Institute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology (BNRist) Department of Automation Tsinghua University Beijing China

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named "deep defense". Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and imageNet) and different DNN architectures. Code and models for reproducing our results are available at https://***/ZiangYan/***.

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Untargeted Backdoor Attack Against Object Detection

Untargeted Backdoor Attack Against Object Detection

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chengxiao Luo Yiming Li Yong Jiang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Research Center of Artificial Intelligence

Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The back-doored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns. Currently, most of the existing backdoor attacks were conducted on the image classification under the targeted manner. In this paper, we reveal that these threats could also happen in object detection, posing threatening risks to many mission-critical applications (e.g., pedestrian detection and intelligent surveillance systems). Specifically, we design a simple yet effective poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns. We conduct extensive experiments on the benchmark dataset, showing its effectiveness in both digital and physical-world settings and its resistance to potential defenses.

关键词： Training Resistance Surveillance Object detection Signal processing Predictive models Robustness

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IoT-Driven image Recognition for Microplastic Analysis in Water Systems using Convolutional neural networks

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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Computer, Communication and Control (IC4), International conference on

作者： M D Ashfaqul Hasan K. Balasubadra G. Vadivel N. Arunfred M.V Ishwarya S. Murugan Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Information Technology University of Technology and Applied Science Salalah Dhofar Oman Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional neural networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics’ influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management.

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Spike-Driven YOLO: Ultra Low-Power Object Detection with Neuromorphic Computing

Spike-Driven YOLO: Ultra Low-Power Object Detection with Neu...

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Mark Barnell Courtney Raymond Lisa Loomis Francesca Vidal Daniel Brown Darrek Isereau Air Force Research Laboratory Information Directorate Rome NY USA SRC Inc. Defense Systems & Solutions North Syracuse NY USA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

The latest Intel neuromorphic processor, Loihi 2, provides a breakthrough in artificial Intelligence (AI) for computing at the edge, where sensor information is collected. The computing architecture does this by leveraging computations at the transistor level in a fashion analogous to the human brain's biological neural networks (vs. a Von Neumann compute architecture). The Loihi 2 high performance, small form factor, and low-power consumption make it well-suited for a wide-range of real-time, deep learning applications such as target classification, object detection, and more. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various ground and airborne platforms' applications. The recently released Loihi 2 ecosystem and the thorough research study completed on this effort were combined to accelerate development, optimization, and demonstration of a new concept of operation for machine learning at the edge. This 2024 research included training and testing Spike-driven YOLO models on data from various sensors. Our concept uses representative sensor data to detect and classify targets of interest through a combination of image processing techniques and machine learning. Importantly, our technical approach allowed us to rapidly train and evaluate the performance of several models for benchmarking against current state-of-the-art algorithms - w/mean average precision > 93% in some cases. The use of Intel's latest Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on several sensor platforms with wide extensibility to other domains that can use this neuromorphic-computing hardware. In summary, this research included the use of new computing frameworks, processing algorithms, and a unique concept of operation.

关键词： YOLO Deep learning Training Machine learning algorithms Neuromorphic engineering Biological system modeling Computer architecture Spiking neural networks Internet of Things Transistors

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Facial expression recognition using multi Radial Bases Function networks and 2-D Gabor filters

Facial expression recognition using multi Radial Bases Funct...

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International conference on Digital Information processing and Communications (ICDIPC)

作者： Raid Saabni Triangle Research & Development Center School of Computer Science Department Tel-Aviv Yaffo Academic College Tel-Aviv Israel

Facial expression analysis and recognition have been researched since the 17'th century. The foundational studies on facial expressions, which have formed the basis of today's research, can be traced back to few centuries ago. Precisely, a detailed note on the various expressions and movements of head muscles was given in 1649 by John Bulwer(1). Another important milestone in the study of facial expressions and human emotions, is the work done by the psychologist Paul Ekman(2) and his colleagues. This important work have been done in the 1970s and has a significant importance and large influence on the development of modern day automatic facial expression recognizers. This work lead to adapting and developing the comprehensive Facial Action Coding System(FACS), which has since then become the de-facto standard for facial expression recognition. Over the last decades, automatic facial expressions analysis has become an active research area that finds potential applications in fields such as Human-Computer Interfaces (HCI), image Retrieval, Security and Human Emotion Analysis. Facial expressions are extremely important in any human interaction, and additional to emotions, it also reflects on other mental activities, social interaction and physiological signals. In this paper, we proposes an artificial neural Network (ANN) of two hidden layers, based on multiple Radial Bases Functions networks (RBFN's) to recognize facial expressions. The ANN, is trained on features extracted from images by applying a multi-scale and multi-orientation Gabor filters. We have considered the cases of subject independent/dependent facial expression recognition using The JAFFE and the CK+ benchmarks to evaluate the proposed model.

关键词： Feature extraction Face Face recognition Training Radial basis function networks Face detection Clustering algorithms

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Optimization of Aquaponics System Efficiency Based on artificial Intelligence Approach

Optimization of Aquaponics System Efficiency Based on Artifi...

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Computing and Machine Intelligence (ICMI), International conference on

作者： Jayden Liu William Jiang The Harker School San Jose USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper aims to design and implement an ML-based approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and artificial neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Wireless communication Wireless sensor networks Accuracy Databases image processing Computational modeling Sensors

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Integrating Spatial Information into Global Context: Summary Vision Transformer (S-ViT)

Integrating Spatial Information into Global Context: Summary...

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Proceedings of the Digital image Computing: Technqiues and applications (DICTA)

作者： Mohsin Ali Haider Raza John Q. Gan Muhammad Haris School of Computer Science and Electronics Engineering University of Essex Mohamed Bin Zayed University of Artificial Intelligence

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Vision Transformers (ViTs), inspired by transformers originally developed for Natural Language processing (NLP), have demonstrated their capability to outperform Convolutional neural networks (CNNs) through their self-attention mechanism. Despite their advantages, the training of ViTs faces challenges with smaller datasets, primarily due to their lack of inherent inductive biases, a key strength of traditional CNNs. To address this shortcoming, this study introduces a novel plug-and-play method that integrates spatial, hierarchical, and locality information from CNNs into ViTs. We employ ResNet-18 as a feature extractor to enrich the ViT architecture, creating the Summary Vision Transformer (S-ViT). The performance of S-ViT was evaluated across multiple public datasets, including CIFAR-10, CIFAR-100, Pets-37, and Flowers-102, under various training conditions—both from scratch and in transfer learning scenarios. Further, we also compare S-ViT with other plug-and-play methods. Our findings reveal that S-ViT not only surpasses baseline ViT models in terms of accuracy but also demonstrates enhanced flexibility and scalability across different architectures.

关键词： Training Computer vision Adaptation models Scalability Computational modeling Computer architecture Flowering plants Feature extraction Transformers Natural language processing

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IoT and Wireless Sensor Network based Autonomous Farming Robot

IoT and Wireless Sensor Network based Autonomous Farming Rob...

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Emerging Trends in Smart Technologies (ICETST), 2020 International conference on

作者： Arsalan Khan Sumair Aziz Mudassar Bashir Muhammad Umar Khan International Islamic University Islamabad Islamabad Pakistan University of Engineering and Technology Taxila Taxila Pakistan

ISBN: (数字)9781728171135

ISBN: (纸本)9781728171142

Internet of things (IoT) is an emerging technology that shows the future of computing and networking. Agricultural monitoring from a remote location is one of the essential applications of IoT based wireless sensor networks. The IoT based wireless sensor network faces problems due to the dynamic changes in the environment. The number of required sensor nodes increases for monitoring of the vast area. By introducing mobility of all nodes in the IoT based wireless sensor network, we can decrease the number of nodes and thus reducing the cost of the overall system. In this research project, an IoT based mobile robotics network is proposed for farming applications. Master and slave robots incorporate the wireless sensor network and are connected via the NRF protocol for reliable sharing of sensor data. The master robot also transmits this data to the IoT server. The highlighting features of this research include weeds detection through image processing and sensors for gathering light, moisture, humidity, temperature parameters. Robots are also equipped with an ultrasonic sensor for avoiding obstacles during navigation. The proposed computer vision algorithm for weed detection is based on texture feature analysis and artificial neural networks. The algorithm is implemented on Raspberry Pi 3 based single board computer for classification of weed and non-weed images.

关键词： IoT Wireless Sensor Network mobile robot NRF protocol weed detection Raspberry Pi Wireless sensor networks Robot Mobile robots based Ganglia Weeds

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