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检索条件"主题词=Detection Algorithms"

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SeLog: A Log Anomaly detection Method on Log Event and Variable Semantic

SeLog: A Log Anomaly Detection Method on Log Event and Varia...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Cheng Li Guang Chen Xiaoxi Mi Yucheng Zhang Hongwei Zhou Information Engineering University Zhengzhou China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Detecting log anomalies based on semantics is an important approach. However, the existing methods often ignore the semantic information of variables. Variables in logs contain important indicative information, which is of positive significance for anomaly detection. Therefore, this paper proposes a log anomaly detection method based on events and fusion variable semantics, referred to as SeLog. SeLog is based on the method of keyword table and positive antonym table, combined with expert experience, to extract valuable variable information from the log. We use these valuable log variables and events as data sources, use the FastText algorithm to obtain their semantics, and construct an input vector suitable for the Bi-LSTM model to detect log anomalies by learning the semantic information of the log context. Experimental verification shows that the F1 value of the model reaches 99 %, which is better than baseline methods such as DeepLog.

关键词： Training Soft sensors Semantics Vectors Data models detection algorithms Anomaly detection Testing Information systems Context modeling

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A Forest Harmful Plant Goldenrod detection Method Based on Enhanced YOLOv5

A Forest Harmful Plant Goldenrod Detection Method Based on E...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Hengyu Ma Bin Fang Yuting Zhang Tianzhu Qu Fuming Peng Xiaoyu Dai School of Automation Nanjing University of Science and Technology Nanjing China Automation Research Institute.NJCJ Ltd. Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The challenge of removing detrimental forest weeds like goldenrods remains unresolved, with weed detection playing a pivotal role in solving this issue. Factors such as irregular light distribution in wooded areas and the variety of weed characteristics frequently result in reduced precision during weed detection. To address these issues, this paper proposes a deep learning weed detection algorithm based on enhanced YOLOv5. Specifically, more accurate anchor boxes are obtained using the latest clustering algorithm K-means||, the location error of bounding boxes is reduced by improving the SIoU loss function, and the model's anti-interference capability is enhanced by incorporating the squeeze-and-excitation network (SENet) mechanism in the data preprocessing stage. Experimental results show that the improved algorithm outperforms the original YOLOv5 algorithm with an increase of 10.1%, 12.8%, and 11.2% in mean average precision (mAP), precision, and recall, respectively. Moreover, the utilization of the enhanced algorithm in weed detection within the project's demonstration forest yielded a detection accuracy of 99%, validating the effectiveness and superiority of the algorithm.

关键词： YOLO Training Deep learning Accuracy Data preprocessing Clustering algorithms Lighting Forestry Image preprocessing detection algorithms

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Improved YOLOv8s-Based Spray Code detection Algorithm

Improved YOLOv8s-Based Spray Code Detection Algorithm

引用

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

作者： Yin Wang Likun Lu Yali Qi Qingtao Zeng School of Information Engineering Beijing Institute of Graphic Communication Beijing China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

The traditional method of code detection relies on manually extracting barcodes and QR codes from paper, which is a cumbersome process and prone to human error, leading to low detection efficiency and insufficient accuracy. To address these issues, this paper proposes an improved YOLOv8s-based code detection algorithm that incorporates the LSK attention mechanism and the WIoU loss function, enabling automatic recognition and localization of barcodes and QR codes on printing devices. Compared to traditional methods, this algorithm significantly improves detection efficiency and accuracy, reduces manual intervention, and optimizes the debugging process. Experimental results show that the detection accuracy of the algorithm exceeds 0.81 and is capable of maintaining efficient and accurate detection performance in complex backgrounds and diverse printing environments.

关键词： Printing Performance evaluation Accuracy Codes Attention mechanisms QR codes Production Printers detection algorithms Testing

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Lightweight Tunnel Leakage Water detection Algorithm Based on YOLOv8n

Lightweight Tunnel Leakage Water Detection Algorithm Based o...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Shiwen Wang Jiaqing Mo Liang Xu Xinqi Zheng School of Computer Science and Technology Xinjiang University Urumqi China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Tunnel leakage is one of the biggest hazards to shield tunnels, this paper proposes a lightweight tunnel water leakage target detection algorithm that improves YOLOv8n for the shortcomings of the existing tunnel water leakage target detection algorithms, such as poor precision and too many model parameters. To make feature fusion richer and reduce the model parameters, this paper proposes to use a bi-directional feature pyramid network on the head network. In addition, this paper uses multi-scale convolutional attention to adapt to the complex features of the leakage area in the tunnel. Finally, use the dilated reparam block to reduce the computational costs and expand the receptive field. The algorithm in this paper not only improves the detection accuracy compared to the original algorithm, but also significantly reduces the model parameters and computational costs. In addition, the superiority of this paper’s algorithm is verified through ablation experiments and comparisons with other mainstream target detection algorithms.

关键词： Adaptation models Accuracy Computational modeling Object detection Bidirectional control Hazards Computational efficiency Systems simulation detection algorithms

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An Efficient Fire detection Algorithm with Dataset Generation Based on Diffusion

An Efficient Fire Detection Algorithm with Dataset Generatio...

引用

Chinese Automation Congress (CAC)

作者： Gaoyang Wang Hongtao Zheng Duo Xiao College of Control science and Engineering Zhejiang University Hangzhou China School of Information & Electrical Engineering Hangzhou City University Hangzhou China The academy of Edge Intelligence Hangzhou City University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Faced with increasing urbanization and population growth, the hazard of fires has become a critical issue in urban areas worldwide. Therefore, the focus on how to achieve accurate and rapid fire detection using artificial intelligence has intensified. Currently, fire detection algorithms often struggle to balance precision and processing speed. At the same time, high-quality fire detection datasets are extremely scarce, severely limiting the performance of algorithms. To address these challenges, this paper first proposes an enhanced fire detection algorithm framework based on YOLOv8, aiming to comprehensively improve the accuracy and processing speed of detection. This framework integrates Swin Transformer and Slim Neck to enhance model performance, and utilizes WIoUv3 for network training regularization. Additionally, this paper introduces a novel fire detection dataset generation algorithm: Fire-Generator, to address the scarcity of high-quality training data. This tool utilizes a latent diffusion model to guide image generation, enabling the synthesis of training data from textual descriptions or existing images. Experimental evaluations demonstrate that our method outperforms most currently available fire detection algorithms in terms of performance. The versatility of Fire-Generator extends beyond fire detection, demonstrating potential for generating training data across different domains. Through this comprehensive strategy, our model achieves significant results by leveraging both real and synthesized data.

关键词： Training Accuracy Limiting Image synthesis Urban areas Training data Fires Transformers Neck detection algorithms

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HSV histogram-based method for fire detection in smart cities

HSV histogram-based method for fire detection in smart citie...

引用

8th IET Smart Cities Symposium (SCS 2024)

作者： N. N. Apaydin K. Dinc O. Yaman M. Karakose Digital Forensic Engineering Fırat Üniversitesi Elazığ Turkey Computer Engineering Fırat Üniversitesi Elazığ Turkey

ISBN: (数字)9781837243105

Global warming and fires are the big problems we face today. Fires, in particular, have caused loss of life and property worldwide in recent years. Early detection of fires in cities and forest areas is very important. In this study, machine learning methods were utilized for the early detection of fires. Histogram values obtained using the HSV color space were processed for the purpose of fire detection. Separate results were obtained using the Zenmuse and Phantom cameras from the FLAME dataset. It was observed that the HSV color space provided better results than the RGB and grayscale representations. For the Zenmuse Camera, the best result was achieved with the Fine KNN model, with an accuracy rate of 99.90%. For the Phantom Camera, the best result was achieved with the Cubic SVM model, with an accuracy rate of 99.64%.

关键词： Climate change Global warming Fires detection algorithms Smart cities Histograms Image color analysis Accuracy Cameras Urban areas Machine learning Gray-scale Nearest neighbor methods

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Grasp Manipulation Relationship detection based on Graph Sample and Aggregation

Grasp Manipulation Relationship Detection based on Graph Sam...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiayuan Luo Yaxin Liu Han Wang Mengyuan Ding Xuguang Lan National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In multi-object stacking scenarios, exploring the relationships among objects and determining the correct sequence of operations are crucial for robotic manipulation. However, previous algorithms inefficiently combine global and local information, often focusing solely on the local features of objects or the interactions of object features at a global level. This approach leads to imbalanced distribution of features and the generation of redundant or missing relationships in complex scenes, such as multi-object stacking and partial occlusion. To address this issue, we have developed a grasp manipulation relationship detection algorithm called Graph Sampling Aggregation Network for Visual Manipulation Relationship detection (GSAGED). This algorithm assists robots in detecting targets in complex scenes and determining the appropriate grasping order. Firstly, the Positional Encoding Module in GSAGED enhances object feature information by considering global contexts. Secondly, the Graph Sampling Aggregation method effectively integrates global and local information, relieving imbalanced distribution of features. Finally, we applied the developed algorithm to a physical robot for grasping. Experimental results on the Visual Manipulation Relationship Dataset (VMRD) and the large-scale relational grasp dataset named REGRAD demonstrate that our method significantly improves the accuracy of relationship detection in complex scenes and exhibits robust generalization capabilities in real-world applications.

关键词： Visualization Accuracy Stacking Focusing Grasping Encoding detection algorithms

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Research on YOLOv8 detection Algorithm Based on SKAttention Fusion

Research on YOLOv8 Detection Algorithm Based on SKAttention ...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Linpo Shang Yanzuo Chang Yi Chen Liuyi Yu Zhenbang Zeng Jinping Chen Department of Energy and Power Engineering Guangdong University of Petrochem Technology (GDUPT) Maoming Guangdong China Department of Mechanical and Electronic Engineering Gandong University (GDU) Fuzhou JiangXi China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

This study proposes a YOLOv8 model that integrates SKAttentionn mechanism to improve the recognition ability of flames for object detection algorithms. This article analyzes the challenges and requirements of eliminating safety hazards in the current production environment, and introduces the relevant structure of the YOLOv8 network model and the principle of the SKAttentionn mechanism. The model was validated using a self built standard flame dataset, and comparative experiments with ECA and GAM modules were also added to evaluate the detection performance of the YOLOv8 model with SKAttentionn mechanism. The experimental results show that the detection accuracy of the improved YOLOv8 model is higher than that of the original model and the model with ECA and GAM added, which has certain application value.

关键词： Adaptation models Accuracy Fires Production Object detection Manuals Inspection Personnel detection algorithms Standards

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Fast-PCANet For Scene Change detection

Fast-PCANet For Scene Change Detection

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Siqi Shi Pengcheng Xia Nanting Li Youwei Yin School of Automation and Information Engineering Xi'an University of Technology Xi’an China Chongqing Fudi Lithium Battery Co. Ltd Chongqing China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

An efficient scene change detection algorithm based on Fast-PCANet is proposed. Given infrared and visible differential images, image blocks with candidate changed points can be distinguishes roughly from the unchanged pixel points by the calculated pixel classification threshold. As for Fast-PCANet which preserves detailed information and improves detection efficiency, feature maps of two principal component analysis convolutional layers are merged and concatenated to one dimensional feature vector, then a linear support vector machine is used to predict those changed pixels. The morphological closed operation and median filter are used to further correct false positives. For training Fast-PCANet, the proportion of negative samples is increased by sampling pseudo-changed points with a second-order arithmetic sequence, which can achieve the balance of between positive and negative samples. Experimental results shows the proposed algorithm effectively eliminates missed and false detection and and improves detection performance.

关键词： Training Support vector machines Change detection algorithms Merging Feature extraction Vectors detection algorithms Robots Principal component analysis Arithmetic

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Research on Lightweight Fire detection Algorithm Based on YOLOv5s

Research on Lightweight Fire Detection Algorithm Based on YO...

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Wei Tang He Zhang YuJie Hou AoBo Duan YingWei Liu GuoQing Zhou Qian Li XiaoHu Zheng College of Electrical and Control Engineering Shaanxi University of Science & Technology Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Aiming at the problems of complex structure and heavy calculation of existing fire detection algorithms, a lightweight fire detection algorithm based on YOLOv5s is proposed in this paper. Considering the characteristics of rapid fire spread, the algorithm specifically optimizes the Backbone and Neck structure of YOLOv5s network to achieve rapid and accurate identification of initial flame and smoke. The basic idea of the algorithm is as follows: Firstly, the Conv module and C3 structure in C3darknet53, the backbone network of YOLOv5s, are replaced with GhostConv and C3Ghost respectively, and auxiliary feature maps are introduced to expand the receptor field of the backbone network and improve the detection efficiency; Secondly, Gsconv convolution is used to replace the conventional convolution operation, which can reduce the number of parameters and computation of the neck network while maintaining the semantic information connection of the channel. The experimental results show that the proposed algorithm reduces the number of parameters by 44.4% and the amount of computation by 53.2%, while maintaining the stability of its performance, which makes the algorithm suitable for resource-constrained environment and can detect flame smoke quickly and accurately.

关键词： YOLO Training Convolution Semantics Fires Reliability engineering Neck detection algorithms Thermal stability Robots

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