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Lightweight real-time detection model for multi-sheep abnormal behaviour based on yolov7-tiny

Lightweight real-time detection model for multi-sheep abnorm...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Zhang, Haotian Ma, Yuan Wang, Xiaobo Mao, Rui Wang, Meili Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9781665491907

animal. Animal behaviour recognition is a vital part of automated farming systems. Although image-based deep learning algorithms can accurately identify animal behaviour, the lack of data on animal abnormal behaviour makes the practical deployment of models of limited significance. At the same time, the ageing of farm monitoring equipment is also a key factor hindering automated farming. This paper constructs a sheep abnormal behaviour dataset ABSB to address these issues and proposes a lightweight real-time multi-sheep abnormal behaviour detection model YOLOv7-Lrab based on the YOLOv7-tiny network. The abnormal behaviour dataset includes four normal behaviours: standing, lying, eating and drinking, and three abnormal behaviours: lameness, attack and death. In the proposed YOLOv7-Lrab model, the small target detection layer, Coordinate attention module, SPD-Conv and Mobileone module are added compared to YOLOv7-tiny. The experimental results show that with a 7:3 ratio of training data to test data, 96.5% recognition accuracy and 95.5% recall can be achieved, and the model size is only 4.5MB with fps of 156. The model is compressed to a minimum without loss of accuracy, providing a new idea for deploying deep learning model in practical application scenarios.

关键词： Animals

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Tomographic reconstruction from sparse-view and limited-angle data using a generative adversarial network 16

Tomographic reconstruction from sparse-view and limited-angl...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Ayad, Ishak Tarpau, Cecilia Cebeiro, Javier Nguyen, Mai K. CY Cergy Paris Univ ETIS UMR 8051 Cergy Pontoise France CY Cergy Paris Univ CNRS ENSEA AGM UMR 8088 Cergy Pontoise France Heriot Watt Univ Maxwell Inst Math Sci Sch Math & Comp Sci Edinburgh Scotland Univ Nacl San Martin Ctr Matemcit Aplicada Inst Tecnol Ciencias Aplicadas ITECA UNSAM CONICETEscuela Ciencia & Tecnol Buenos Aires Argentina CY Cergy Paris Univ ENSEA CNRS ETIS UMR 8051 Cergy Pontoise France

ISBN: (纸本)9781665464956

Limited-angle and sparse-view computed tomography have been widely used to shorten the acquisition time in medical imaging and to offer the possibility of scanning large objects. However, this is a severely ill-posed inverse problem due to missing data. In these scenarios, the well-known filtered back-projection reconstruction technique exhibits severe artifacts and degradation. Recently, deep learning methods have demonstrated impressive performance in computer vision (denoising, classification, etc.) but it frequently fails to solve both limited-angle and sparse-view reconstruction. Inspired by the high performance of GAN-based image-to-image translation methods, we investigate a patchGAN as a solution to the reconstruction problem mapping data (Radon space) into the image domain. The generator is made of a V-net where the reconstruction in the sense of a least-squares minimization is carried out at different scales in the encoder path and linked with the decoder path by skip connections. The discriminator uses both information from the image and projection data domains. The proposed method gives promising reconstruction results from data acquired with a limited angular range covering only 110 degrees (instead of 180 degrees), as well as for sparse-view data with 10 degrees of sampling step. Moreover, different reconstruction results show that the method is able to reconstruct images from sparse and limited angular range data at the same time.

关键词： computed tomography (CT) generative adversarial networks (GAN) image reconstruction inverse problem hybrid representations

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Proceedings of the 18th INDIAcom;2024 11th international conference on Computing for Sustainable Global Development, INDIACom 2024

Proceedings of the 18th INDIAcom;2024 11th International Con...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

ISBN: (纸本)9789380544519

The proceedings contain 310 papers. The topics discussed include: blockchain technology and artificial intelligence based integrated framework for sustainable supply chain management system;contribution of microbial mechanism and artificial intelligence in wine production;a study of various clustering algorithms for image segmentation;deep learning based energy-efficient task scheduling in cloud computing;enhancing e-commerce fashion sales through personalized recommendation systems;an investigation of the intelligent and secure child rescue system from borewell;design of a dual band microstrip patch antenna;ml-based blood pressure estimation using converted ppg signal from video;auditing of outsourced data in cloud computing: an overview;and interpretable machine learning models for credit risk assessment.

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Revolutionizing Healthcare: Federated Learning for Large-Scale image Processing in IoT-Integrated systems 5

Revolutionizing Healthcare: Federated Learning for Large-Sca...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ravi, Kiran Chand Vadlamani, Subramanyam M Kumarbasu, Asim Kumar, Vivek Mallik, Satadal Sreelatha, K. Mlr Institute of Technology Department of Ece Hyderabad India Anurag Engineering College Department of Cse Kodad India The Neotia University West Bengal Kolkata India Department of Physics Andhrapradesh Eluru534003 India

ISBN: (纸本)9798331504403

The internet of things (IOT) devices integrated into federated learning (FL) is a revolutionary concept in the fast-paced world of healthcare, especially in medical image processing. In this paper, we propose a novel framework with the enablement of the internet of Things (IoT)-based medical devices and Federated Learning to autonomously handle large-scale medical images in an efficient and secure way. The approach adopts Cascaded Convolutional Neural Networks with Spatial-Variant Convolution Kernel (CCSVCK) for improved feature extraction and classification performance. The framework de-centralizes the training process and thus allows cohering to patient privacy and other strict data protection regulations. We used a large dataset of mixed real and synthetic medical images after various preprocessing steps, such as normalization, augmentation, and anonymization, to implement and experiment on all stages of our approach. Training iterations were launched on each local node and contributed model updates to a central coordinator for aggregation. At this point in time, the model was synchronized across all nodes again and iteratively a global model was continuously computed. Evaluation metrics confirmed the robustness of the framework, yielding a commendable accuracy of 97.2%. This novel approach is a blend of not only providing better accuracy in diagnosis but also taking care of the privacy of the information, which has great potential to change health care diagnostics into IoT and Federated Learning. © 2024 IEEE.

关键词： Differential privacy

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DOA estimation based on SC-FDMA signal 2

DOA estimation based on SC-FDMA signal

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2nd international conference on signal image Processing and Communication, ICSIPC 2022

作者： Chen, Rui Song, Yaoliang Wang, Jinyu School of Electronic & Optical Engineering Nanjing University of Science & Technology China

ISBN: (纸本)9781510657694

Communication and sensing are the basic functions of IOV (internet of Vehicle), it is an efficient and economical way to realize radar sensing function by using communication signal. In this paper, it is proposed a target DOA estimation scheme based on a series of super-resolution algorithms in which the 5G upstream SC-FDMA signal is used as the radar probing signal and a uniform linear array is used to receive the target echo. Modeling analysis and simulation results show that the proposed scheme can accurately find the direction of radar target. © 2022 SPIE.

关键词： Radar

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Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods 2

Classification and recognition of soybean leaf diseases in M...

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2nd international conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and signal Processing, PCEMS 2023

作者： Dixit, Shriniket Kumar, Anant Haripriya, Akash Bohre, Khitij Srinivasan, Kathiravan SCOPE Vellore India School of Bioscience and Technology Vellore India Oriental College of Science and Technology SCOPE Bhopal India Acropolis Institute of Technology SCOPE Indore India

ISBN: (纸本)9798350310719

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach's feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively. © 2023 IEEE.

关键词： image enhancement

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Joint embedding of graph-guided features for radiology report summarization

Joint embedding of graph-guided features for radiology repor...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Sun, Dengdi Fan, Xuyang Luo, Bin Ding, Zhuanlian School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

ISBN: (纸本)9781510680425

Radiology report summarization is a core part of the computer-aided-diagnosis, which aims to automatically generate examination impressions to accurately summarize key medical information in basic radiology reports. Most existing studies improve the accuracy of report summarization by deeply mining internal connections of basic radiology reports. However, these methods simply regard radiology report summarization as a simple text-to-text single-modality task, ignoring the indispensable role of raw medical images in the entire report summary work. To address this issue, we propose a novel radiology report summarization framework based on joint embedding of graph-guided features. Our method uses a Topic Graph to accurately capture disease information in medical images, and a Word Graph to deeply mine important relationships between keywords in radiology reports. By using Graph Convolutional Networks (GCN) to model the key information of the two modalities, we successfully achieve effective fusion of the two features and ultimately generate highly accurate report impressions. Experimental results on two benchmark datasets OPEN-I and MIMIC-CXR demonstrate the effectiveness of our method. Our method demonstrates superior performance over existing works on multiple evaluation metrics. © 2024 SPIE.

关键词： Graph embeddings

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Robustness of Deep Learning enabled IoT Applications Utilizing Higher Order QAM in OFDM image Communication System 5

Robustness of Deep Learning enabled IoT Applications Utilizi...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Islam, Nazmul Ahmad, Ijaz Shin, Seokjoo Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

ISBN: (纸本)9781665456456

In a standard Orthogonal Frequency Division Multiplex (OFDM)-based communication system, higher order M-ary Quadrature Amplitude Modulation (M-QAM) increases throughput and link capacity significantly due to the larger number of bits per symbol. Such a system is suitable to transmit large volumes of image data for real-time internet of Things (IoT) applications. However, higher order M-QAM increases the noise margin and Bit Error Rate (BER) in the received signal. Although the error bits corrupt the individual pixels values, the global contents of the image may remain intact and can be interpreted by Deep Learning (DL) models. Therefore, the current study analyzes the robustness of DL models for image communication systems utilizing higher order M-QAM. The performance analysis of the system is presented in terms of the BER and image quality measures. In the analysis, distortions in the reconstructed images decrease significantly when using higher E-b/N-0 across all M-QAM. For higher E-b/N-0 in the 1024-QAM system, the performance of the model improved by 11% and obtained an accuracy of 86%, lagging by 6% compared to the accuracy obtained on the original images. Overall, the model maintained a high accuracy range between 83% and 90% at E-b/N-0 10 dB across all M-QAM.

关键词： OFDM System QAM Modulation Deep learning

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Advances in Artificial Intelligence, Big Data and Algorithms - Proceedings of the 3rd international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

Advances in Artificial Intelligence, Big Data and Algorithms...

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3rd international conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

ISBN: (纸本)9781643684444

The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout systems;application of lightweight emotion recognition model in intelligent construction site monitoring;research on the application of smart wearable in the emotional management of the elderly;artificial intelligence technology in the field of broadcasting and hosting;intelligent inspection method for photovoltaic modules based on image processing and deep learning;application of AI algorithms in power system load forecasting under the new situation;research on the intelligent operation and maintenance control system of power distribution network based on big data technology;research progress of intelligent rail transit train control system;and design and implementation of intelligent potted plant management system based on internet of Things.

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LDA-Felzenszwalb Guided Superpixel Graph Convolutional Networks for UAV Hyperspectral image Classification 12

LDA-Felzenszwalb Guided Superpixel Graph Convolutional Netwo...

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12th international conference on Information systems and Computing technology

作者： Huang, Lehao Liu, Jianchen Jiang, Linhuan Zhang, Bingru Zhang, Zhen Kunming Univ Sci & Technol Kunming Yunnan Peoples R China Shandong Univ Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Unmanned aerial vehicle (UAV) hyperspectral imagery, distinguished by its exceptional spatial granularity and rich spectral diversity, is widely utilized in urban planning, vegetation monitoring. UAV hyperspectral images classification is a crucial application for facilitating feature monitoring. However, the complex textures and low signal-to-noise ratio inherent in UAV hyperspectral images make their classification a formidable challenge. Hence, in this article, an innovative classification method that integrates a Graph Convolutional Network (GCN) with Linear Discriminant Analysis-Felzenszwalb (LDA-Felzenszwalb) superpixel segmentation method was proposed. Firstly, the UAV hyperspectral images are clustered based on the LDA-Felzenszwalb algorithm, which employs downscaling and spectral-spatial similarity. Then, the encoded superpixel images are subjected to feature extraction via graph convolutional networks, aiming to uncover the latent spatial topological relationships within the data. Ultimately, the UAV hyperspectral images are classified with high precision. The effectiveness of the proposed method is demonstrated using the WHU-HI-HongHu dataset, where it achieves an overall classification accuracy of 92.41%, outperforming comparison methods by at least 11.38%. The results show the classification method is highly effective when applied to high spatial resolution hyperspectral images.

关键词： Graph Convolutional Network Superpixel Segmentation Algorithm Unmanned Aerial Vehicle Hyperspectral imagery image Classification

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