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检索条件"主题词=Detection Algorithms"

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A novel method of detection of Hot spots in Solar Panels using Deep learning-based Object detection Algorithm

A novel method of Detection of Hot spots in Solar Panels usi...

引用

BIT Conference (BITCON), International

作者： Rakesh Rohan D K Tanti Mukesh Chandra Department of Electrical Engineering BIT Sindri Dhanbad Jharkhand Production and Industrial Engineering BIT Sindri Jharkhand

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The formation of hot spots in solar panels is a critical aspect of a photovoltaic system. These anomalies can significantly affect the efficiency and longevity of the panels. In this paper, a deep learning-based objection detection algorithm is used to detect the hot spots in solar panels in a more efficient and easy way. The hotspot dataset is collected using a thermal camera from installed solar panels of BIT Sindri, Dhanbad. The thermal images are manually annotated using Roboflow. The mean average precision (mAP) for the different classes of hotspot achieved a satisfactory result. The object detection technique YOLO has proven highly responsive, highlighting its suitability for practical use for preventative maintenance of photovoltaic panels.

关键词： YOLO Cameras Real-time systems Maintenance Solar panels detection algorithms

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SM-LightNet: A Lightweight Ship detection Algorithm Based on Improved YOLOv8

SM-LightNet: A Lightweight Ship Detection Algorithm Based on...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Zhiguang Gao Xianwei Rong Xiaoyan Yu School of Computer Science and Information Engineering Harbin Normal University Harbin China School of Physics and Electronic Engineering Harbin Normal University Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

This article presents a streamlined ship detection algorithm SM-LightNet based on YOLOv8 improvement, which is suitable for environments such as ports with dense ship traffic and complex maritime navigation. Firstly, we introduce a Slim-neck structure that includes a lightweight convolution called GSConv and VoVGSCSP structure. It replaces the traditional convolution in the Neck part of YOLOv8 with GSConv, achieving real-time detection of edge devices and effectively reducing the complexity of computation and parameters. Secondly, replacing the C2f structure in Neck with VoVGSCSP enables the model to have a more concise network structure, which can effectively fuse feature information and enhance the precision and stability of object detection. Finally, the loss function MPDIoU was introduced, which calculates IoU by minimizing the point distance between the estimated boundary box and the actual boundary box, simplifying the calculation process and improving detection accuracy. The outcomes of the experiments indicate that the accuracy of the lightweight algorithm reaches 98.2%, and the GFLOPs of the algorithm are 7.4, which is 9.5% lower than the performance of the original model. The model parameter size is 2.80M, which is 7.5 % lower than the original model.

关键词： Accuracy Convolution Computational modeling Object detection Feature extraction Stability analysis Real-time systems Neck Marine vehicles detection algorithms

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Analyze the Techniques of Community detection in Social Networks and Their Applications

Analyze the Techniques of Community Detection in Social Netw...

引用

Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Sonia Choudhary Seema Malik R. K. Yadav Dept. of CSE Raj Kumar Goel Institute of Technology Uttar Pradesh India Dept. of Industrial IOT Vivekananda Institute of Professional Delhi India Dept. of ECE Raj Kumar Goel Institute of Technology Uttar Pradesh India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The World Wide Web's emergence paved the way for various social networking sites, facilitating easy communication and connection among users on a shared platform. A social network is essentially a graph comprising nodes and edges, representing users and their interactions, respectively. Common interests naturally draw people together, fostering the formation of communities within social networks. This community formation, in turn, leads to community detection, a process applicable in diverse fields such as finance, politics, marketing, education, and the medical field. This paper explores current3 algorithms for detecting communities in social networks and delves into potential algorithms utilizing deep learning for this purpose.

关键词： Deep learning Social networking (online) Image edge detection Semantics Education Finance Evolutionary computation Web sites Data mining detection algorithms

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Adaptive Scene Change Safety Helmet Wearing detection Algorithm

Adaptive Scene Change Safety Helmet Wearing Detection Algori...

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Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Xiangming Qi Yuhan Li School of Software Liaoning Technical University Huludao China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Wearing a safety helmet is a basic measure to reduce or avoid accident losses. However, factors such as changes in lighting and target occlusion can cause instability in various test result indicators. To address this issue, a helmet-wearing detection algorithm based on adaptive scene changes is proposed. Taking YOLOv8n as the baseline, the SE attention mechanism is embedded in the basic unit C2f to adjust channel weights, capturing more details and enhancing the ability for multi-scale feature extraction and fusion. The BiFormer attention mechanism is embedded at the front end of the detection head, utilizing dual-layer routing attention to improve target feature capture, enhance detection head performance, and strengthen the algorithm's adaptive scene change capability. The baseline algorithm's loss function, CIoU, is replaced with Inner IoU, focusing more on overlapping areas within the bounding box. This improves detection accuracy, accelerates convergence, and enhances generalization ability. Additionally, the Lion optimizer is used to optimize the algorithm, improving gradient descent performance, reducing memory consumption and computational complexity, and increasing detection speed. Ablation experiments and comparative experiments on the self-made Adaptive Scene dataset show that, compared with the baseline algorithm, Precision, Recall, and mAP are improved by 3.1%, 5.3%, and 4.4 %, respectively, demonstrating the superiority of the proposed algorithm.

关键词： Training Head Attention mechanisms Feature extraction Routing Loss measurement Robustness Safety Classification algorithms detection algorithms

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Object detection in High-Resolution Aerial Images Using Single Shot Multi-Box Detector

Object Detection in High-Resolution Aerial Images Using Sing...

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Data Science and Network Security (ICDSNS), International Conference on

作者： S. Gobinath Muntather Almusawi G Shalini Bollipelly Pruthviraj Goud K Sudheer Kumar Department of Information Technology V.S.B. Engineering College Karur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology Anurag University Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In recent decades, the detection of objects plays a vital role in High-Resolution Aerial Images and extensive range applications such as environmental managing, urban development and military applications. The obj ect detection in high resolution aerial images has improved with advanced technologies. However, there is still challenging task due to various appearances and scales of the obj ects. A Single Shot Multi-Box Detector Method (SSDM) is proposed in this paper for higher detection precision of Objects in High-Resolution Aerial Images. The Histogram Equalization (HE) is used for augmenting the images of RSOD dataset and Otsu method is used for segmentation process. Next, feature extraction is carried out by Gray Level Co-occurrence Matrix (GLCM) method. And lastly the proposed SSDM method is used for detecting the obj ects in high resolution aerial images. The proposed SSMD technique is evaluated using the RSOD dataset and achieved 99.7% of precision, 97.3% of recall and 99.1 % of map respectively. The proposed SSDM method outperformed when compared with existing methods such as KCFS-YOLOv5 and LAR-YOLOv8.

关键词： YOLO Image segmentation Histograms Image resolution Databases Detectors Network security Data science Feature extraction detection algorithms

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Fire detection Based on Improved YOLOv8 Network Mode

Fire Detection Based on Improved YOLOv8 Network Mode

引用

International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Longling Shao Wanjun Yu Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

In the traditional field of fire detection, a significant amount of specialized equipment and highly trained professionals are typically required. However, the operational efficiency of these methods often tends to be low, and they are susceptible to false alarms, making it challenging to effectively meet the urgent demands of routine fire safety inspections. Additionally, current fire detection technologies based on neural network image recognition also face challenges in terms of realtime performance and accuracy. To enhance the performance of fire detection, this study proposes a fire detection algorithm based on improved YOLOv8, termed FD-YOLOv8 (Fire detection- YOLOv8). On a self-built fire dataset, Fire_dataset, the mean Average Precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5 to 0.95 are 53.6% and 30.7%, respectively.

关键词： Measurement Accuracy Convolution Neural networks Interference Inspection detection algorithms Informatics Optimization Fire safety

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MRM-LOOP:A Dynamic Visual Loop Closure detection Algorithm Based on Motion Region Matching

MRM-LOOP:A Dynamic Visual Loop Closure Detection Algorithm B...

引用

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhiming Zhang Jun Zhang Yuansheng Liu Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China College of Robotics Beijing Union University Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Aiming at the problem of false negatives and false positives in loop closure detection for visual SLAM algorithms in dynamic environments, a dynamic visual SLAM loop closure detection algorithm based on motion region matching named MRM-LOOP is proposed. Firstly, the VLAD local feature descriptor algorithm is used to extract the feature vectors of image feature points to ensure the stable correspondence between different images; Secondly, it uses the inter-frame motion vectors extracted from IMU data to enhance the robustness of feature matching and avoid false positive detection caused by camera pose mismatching; Finally, a region-weighted matching strategy is introduced to achieve more accurate loop closure detection by comparing the vector matching degree between the current frame and the historical frame and calculating the matching weight. The experimental results show that the proposed method can significantly improve the recall rate of loop detection in the open data set while maintaining the same accuracy, and the true positive detection probability is increased by 27.29% compared with the DBOW bag method when facing camera rotation. Correct loop detection can be realized in real dynamic environment data set. This paper provides a new solution for visual SLAM technology in dynamic environment.

关键词： Visualization Simultaneous localization and mapping Accuracy Heuristic algorithms Dynamics Feature extraction Vectors Robustness Data mining detection algorithms

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Voltage detection Algorithm for Characteristics of Grid-Forming Converters Under Voltage Drop Events

Voltage Detection Algorithm for Characteristics of Grid-Form...

引用

Power Energy Systems and Applications (ICoPESA), International Conference on

作者： Guoping Huang Qiao Shi Junfeng Liu Feng Liao Foshan Power Supply Bureau of Guangdong Power Grid Co. Foshan Guangdong School of Automation and Science and Engineering South China University of Technology Guangzhou Guangdong

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

Clarifying the impact and output voltage of the grid- forming converter under grid voltage drop events is a key step in voltage drop management. Focusing on the grid-forming converter with a control structure based on virtual synchronous generator technology and the current limiting section, this paper explores the operating mechanism of the grid-forming converter during voltage drops and the characteristic waveforms and variations of the output voltage under grid voltage drop events are obtained. Aiming at the waveform characteristics of the output voltage, a voltage detection algorithm based on the coordinate-free transform method and morphological filters is proposed, which eliminates the intermediate section of constructing the virtual three-item voltage, and can directly separate the direct flow of the voltage signals. The morphological filters can better extract the cosine periodic fluctuation and change components in the characteristic waveforms of the voltage. Finally, a detection model is built using Simulink, the model verifies the feasibility and effectiveness of the new detection algorithm combined coordinate- free transform method with the morphological filter, as well as its high adaptability to the characteristic waveforms of the grid- forming converter.

关键词： Adaptation models Filters Fluctuations Limiting Focusing Transforms Synchronous generators detection algorithms Voltage control Grid forming

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Research on Apple Fruit detection Algorithm Based on Improved YOLOV8

Research on Apple Fruit Detection Algorithm Based on Improve...

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Algorithm, Image Processing and Machine Vision (AIPMV), International Conference on

作者： Yuyang Zhang Jing Jiang Shan Fu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

To address the issues of complex background interference, multi-scale targets and poor detection accuracy of overlapping targets in apple image target detection, an apple fruit detection algorithm CGW-YOLOv8 based on improved YOLOv8 is proposed. Firstly, the CBAM attention module is introduced based on the YOLOv8 detection model to enhance the saliency of the fruit in complex environments and reduce the computational cost of the network. Secondly, GFPN effectively combines feature information of different resolutions, so that the model can achieve higher detection accuracy when dealing with multi-scale targets. Finally, WIoU_v3 is used to assign different weights to each feature point to improve the matching accuracy between the predicted box and the real box, while significantly improving the positioning accuracy. The test results on the apple dataset show that this method achieves an average accuracy of 87.3 %, with an mAP improvement of 3.5% compared to the original YOLOv8 algorithm. The frame rate FPS is 357 frames per second, achieving real-time detection.

关键词： Accuracy Target recognition Computational modeling Machine vision Object detection Feature extraction Prediction algorithms Real-time systems detection algorithms Optimization

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Improved YOLOv8 detection Algorithm of Contraband in X-Ray Security Inspection Image

Improved YOLOv8 Detection Algorithm of Contraband in X-Ray S...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Zhaoyuan Chen Yan Xu Fang Li Guorong Liu Ping Hu Xiaodi Zhang College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Personnel safety is particularly important in public places where security checks are required, such as airports, train stations and subway stations. In view of the features of disorderly placement of items, blocking of contraband and intentional hiding in X-ray security inspection images, this research proposes an improved YOLOv8 network model combined with Mixup data augmentation, Biformer dual-layer routing attention modules, and C2f_f modules to assist security personnel in conducting efficient security inspections. At the input stage, the Mixup data augmentation method is employed to enhance the model's ability to learn to detect obstructed and concealed prohibited items. The Biformer dual-layer routing attention modules are introduced into the backbone network to enhance the model's focus on foreground information and improve its recognition ability. The C2f_f modules are incorporated into the backbone network and Neck section to improve the model's feature representation capability and enhance overall network performance. Testing on the Easy, Hard, and Hidden datasets of PIDray yields mAPs of 85.2%, 86.2%, and 68.3% respectively. Experimental results demonstrate an increased detection accuracy of 2.3%, 1.4%, and 2.2% respectively on the three prohibited item recognition test sets for the improved YOLOv8 model.

关键词： Accuracy Inspection Data augmentation Routing Data models Neck Security Personnel detection algorithms X-ray imaging

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