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FDD-YOLO: A Novel detection model for Detecting Surface Defects in Wood

FORESTS 2025年第2期16卷 308-308页

作者： Wang, Bo Wang, Rijun Chen, Yesheng Yang, Chunhui Teng, Xianglong Sun, Peng Hechi Univ Sch Artificial Intelligence & Smart Mfg Yizhou 546300 Peoples R China Hechi Univ Key Lab AI & Informat Proc Yizhou 546300 Peoples R China Guangxi Normal Univ Sch Teachers Coll Vocat & Tech Educ Guilin 541004 Peoples R China Guangxi Univ Engn Res Ctr Agr & Forestry Intelligent Equipment Guilin 546300 Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China

Wood surface defect detection is a critical step in wood processing and manufacturing. To address the performance degradation caused by small targets and multi-scale features in wood surface defect detection, a novel deep learning model is proposed in this study, FDD-YOLO, specifically designed for this task. In the feature extraction stage, the C2f module and the funnel attention (FA) mechanisms are integrated into the design of the C2f-FA module to enhance the model's ability to extract features of wood surface defects of various sizes. Additionally, the Dual Spatial Pyramid Pooling-Fast (DSPPF) module is developed, and the Context Self-attention Module (CSAM) is introduced to address the limitations of traditional max pooling methods, which often overlook global contextual information when extracting local features, thereby improving the detection of small-scale wood defects. In the feature fusion stage, a Dual Cross-scale Weighted Feature-fusion (DCWF) module is proposed to fuse shallow, deep, and cross-scale features through a weighted summation approach, effectively addressing the challenge of scale variation in wood surface defects. Experimental results demonstrate that the proposed FDD-YOLO model significantly improves detection performance, increasing the mAP of the baseline model YOLOv8 from 78% to 82.3%, a substantial enhancement of 4.3 percentage points. Furthermore, FDD-YOLO outperforms other mainstream defect detection models in terms of detection accuracy. The proposed model demonstrates significant potential for industrial applications by improving detection accuracy, enhancing production efficiency, and reducing material waste, thereby advancing quality control in wood processing and manufacturing.

关键词： detection model funnel attention mechanism dual spatial pyramid pooling-fast dual cross-scale weighted feature fusion wood surface defect

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DANTD: A Deep Abnormal Network Traffic detection model for Security of Industrial Internet of Things Using High-Order Features

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IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 21143-21153页

作者： Shi, Guolong Shen, Xinyi Xiao, Fuke He, Yigang Anhui Agr Univ Sch Informat & Comp Hefei 230036 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

With the development of blockchain, artificial intelligence, and data mining technology, abnormal network traffic data has become easy to obtain. The traffic detection model detects the traffic patterns in the network to find abnormal traffic that does not conform to the normal traffic law, which has great security significance for Industrial Internet of Things (IIoT) networks and devices in real scenarios. However, previous abnormal detection models rely on expert experience and cannot cope with real-time changes in IIoT scenarios. The manual features cannot be sufficiently representative and adaptive. Moreover, there are few abnormal traffic data in real scenarios, which makes the model unable to fully learn the potential distribution in abnormal data. Therefore, in this work, we propose a deep abnormal network traffic detection model (DANTD) for the security of IIoT using high-order features and novel data augmentation strategies. The DANTD model first adopts a deep convolutional autoencoder to extract effective high-order features to make it more representative. Then, the DANTD model uses generative adversarial networks as data augmentation strategies to enrich the abnormal data, so that the model can fully consider the information of the data distribution. Comprehensive experiments on real IIoT data sets validate the effectiveness of the DANTD model.

关键词： Industrial Internet of Things Data models Feature extraction Brain modeling Computational modeling Security Predictive models Convolutional neural networks (CNNs) detection model generative adversarial networks (GANs) Industrial Internet of Things (IIoT)

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Hybrid Malware Variant detection model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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Computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Genetic algorithm based production knowledge base for mechanical fault detection model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第3期23卷 1251-1263页

作者： Shen, Yang Guangzhou Inst Technol Coll Informat Engn Guangzhou 510075 Guangdong Peoples R China

Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection.

关键词： detection model genetic algorithm mechanical fault production knowledge base

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Optimization of Network Intrusion detection model Based on Big Data Analysis

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Journal of Mobile Multimedia 2024年第6期13卷 1357-1378页

作者： Shan, Jizhou Ma, Hong Hainan College of Economics and Business Hainan Haikou 571127 China

As user usage grows, so do security threats to networks, the Internet, websites, and organizations. Detecting intrusions in such a big data situation is complex. A feature-optimized network intrusion detection model based on extensive data analysis is designed to overcome the limitations of current network intrusion detection models and obtain more ideal results. Firstly, the current modeling status of network intrusion detection is studied, and the influence of features on the results of network intrusion detection is analyzed. Then, the feature optimization mathematical model of network intrusion detection is established. The solution of the feature optimization mathematical model is searched by an adaptive genetic algorithm simulating natural biological evolution. The optimal feature subset of intrusion detection is obtained by back coding the optimal solution. Finally, according to the optimal feature subset, the learning sample of network intrusion detection is modeled, and the optimal network intrusion detection model is designed. Using the standard data set of network intrusion detection for simulation and comparison tests, the average accuracy of this paper’s network intrusion detection model is about 95%, while other current network intrusion detection models are below 95%. Meanwhile, the time of training and the detection of intrusion detection modeling in this model is significantly reduced, and better efficiency of network intrusion detection can be obtained. © 2024 River Publishers.

关键词： big data analysis detection model feature analysis learning sample modeling network intrusion detection Network security

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Research on Artificial Intelligence detection model of AC Fault Arc Based on Attention Mechanism 7

Research on Artificial Intelligence Detection Model of AC Fa...

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7th International Conference on Electric Power Equipment - Switching Technology, ICEPE-ST 2024

作者： Sheng, Dejie Lan, Tianle Yu, Jingtao Li, Hai Bao, Zhizhou Wang, Yao School of Electrical Engineering Hebei University of Technology Tianjin300401 China Hebei Institute of Metrology Shijiazhuang050051 China

ISBN: (纸本)9798350388947

The occurrence of low-voltage AC series arc faults will cause the temperature at the fault to rise rapidly, which can easily lead to electrical fires and cause serious losses to individuals and society. However, the detection accuracy of traditional arc fault methods is insufficient and cannot effectively curb the occurrence of arc faults. Artificial intelligence-based technology provides high-precision detection solutions, but the AI model itself is a 'black box'. Once a misjudgment occurs, the root cause of the model error cannot be fundamentally identified, and further improvements in model accuracy are limited. In order to solve the above problems, this paper proposes a new method for AC arc fault detection based on attention mechanism. The introduction of the attention mechanism effectively handles the weight between the input arc data and the model output, thereby improving the accuracy of model detection. Experimental results show that the model proposed in this article achieved a detection accuracy of 9 9. 6 9 %, proving the efficiency of this method. © 2024 IEEE.

关键词： AC arc arc fault attention mechanism detection model model interpretability

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A detection model of the Complex Dynamic Traffic Environment for Unmanned Vehicles

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IEEE ACCESS 2022年 10卷 51873-51888页

作者： Yang, Shijuan Gao, Li Zhao, Yanan Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

It has always been an important and arduous task to detect the complex dynamic traffic environment, especially for unmanned driving. Although the existing advanced detection models have reached the speed requirements for detection, the detection accuracy needs to be further elevated to improve the unmanned driving's safety. How to balance the accuracy and speed of detecting the complex dynamic traffic environment is still the primary problem to be solved for unmanned vehicles. Therefore, this article proposes a detection model of the complex dynamic traffic environment for unmanned vehicles by following the framework idea of YOLOv3. Firstly, we regard MobileNetv3 as the backbone and replace the traditional convolution with the depthwise separable convolution in the whole model to reduce the number of parameters and calculations. Secondly, in enhanced feature fusion layers, we perform the multi-scale fusion of four feature maps by the compress-and-expand module, the SPP module, and the cross-layer bidirectional module of feature fusion to improve the locating accuracy and reduce false detections. Thirdly, we add an IoU loss to improve the accuracy of model regression. Then, we employ the improved clustering algorithm to re-cluster anchor boxes, reducing the time overhead while improving the clustering accuracy. Finally, we compare the proposed model with other advanced detection models in the processed BDD dataset and the KITTI dataset. We verify that the mAP of the proposed model improves notably without loss of detection speed, the number of parameters and calculations decreases dramatically, and the proposed model exhibits a more superior performance.

关键词： Feature extraction Vehicle dynamics Object detection Autonomous vehicles Proposals Convolution Clustering algorithms Complex dynamic traffic environment detection model unmanned vehicles YOLOv3

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Use of Density Probability Distribution for Creating the detection model of Cigarettes' Dense-End Position 4

Use of Density Probability Distribution for Creating the Det...

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4th International Conference on Digital Data Processing, DDP 2024

作者： Ying, Wei Jin, Chunshan Zhu, Zengxu Yu, Xuqin Lou, Feng Zhang, Lihong Wang, Jianwei China Tobacco Zhejiang Industrial Co. Ltd. Zhejiang Province Hangzhou310009 China

ISBN: (纸本)9798331515706

The density distribution of cigarettes was analyzed to improve the detection precision of cigarettes' dense-end position. The density distribution pattern under the ideal production condition was combined, and a dense-end position detection model was constructed. The model parameters were determined based on data optimization. MW-T and cigarettes validated the model. We got the conclusions below. 1) For the measurement of dense-end with the Dense-end Position Standard Parts, the average error of the measurement was 0.1646 mm, and the maximum error was - 0.2735 mm. 2) The average error between the measured value of the dense-end position with ordinary cigarettes and that of the MW4420 was 0.79 mm, which meets the actual production demand (≤1 mm). The model has high accuracy and reliability and can provide favorable technical support for the dense-end position detection of cigarettes. It is helpful to improve the level of quality control in cigarette production and inject new vigour for the sustainable development of the cigarette industry. © 2024 IEEE.

关键词： Cigarette Dense-end position detection Density probability distribution detection model

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Tunnel lining crack detection model based on improved YOLOv5

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2024年 147卷

作者： Duan, Shuqian Zhang, Minghuan Qiu, Shili Xiong, Jiecheng Zhang, Hao Li, Chenyang Jiang, Quan Kou, Yongyuan Zhengzhou Univ Sch Civil Engn Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Henan Urban Planning Inst & Corp Zhengzhou 450044 Henan Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China Jinchuan Grp Co Ltd Jinchang 737100 Gansu Peoples R China

An improved real-time tunnel lining crack detection model based on YOLOv5 is proposed. This model maintains high precision and accuracy crack detection in low-light, low-contrast and high-noise environments by introducing several effective data augmentation techniques as well as semantic context encoding (SCE) and detail preserving encoding (DPE) at the head of the network structure. It achieves 90 % precision, 91 % recall, and 92 % mAP@50. The model demonstrates better detection performance than YOLOv4-tiny, YOLOv5s, YOLOv8s, and traditional threshold segmentation method, especially in complex environments to reduce misdetection and omission. The average detection time is only 12 ms per image, demonstrating the feasibility of its real-time application. The robust and generalization performance of the model is validated in specific engineering applications, showing great potential for improving detection efficiency, cost-effectiveness, and reliability in tunnel safety assessment and disasters management.

关键词： Tunnel lining cracks detection model Intelligent recognition YOLOv5 Computer vision

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EW-YOLOv7: A Lightweight and Effective detection model for Small Defects in Electrowetting Display

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PROCESSES 2023年第7期11卷 2037页

作者： Zheng, Zihan Chen, Ningxia Wu, Jianhao Xv, Zhixuan Liu, Shuangyin Luo, Zhijie Zhongkai Univ Agr & Engn Coll Informat Sci & Technol Guangzhou 510225 Peoples R China Zhongkai Univ Agr & Engn Intelligent Agr Engn Res Ctr Guangzhou 510225 Peoples R China Zhongkai Univ Agr & Engn Guangzhou Key Lab Agr Prod Qual & Safety Traceabil Guangzhou 510225 Peoples R China

In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computational costs are reduced by 19.2% and 64.3%, respectively. Compared with current state-of-the-art detection network models, the proposed EW-YOLOv7 exhibits superior performance in detecting electrowetting display defects. This model helps to solve the problem of defect detection in industrial production of electrowetting display and assists the research team in quickly identifying the causes and locations of defects.

关键词： electrowetting display YOLOv7 detection model GhostNetV2 small defects

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