Synthetic Aperture Radar (SAR) is capable of producing high-resolution complex-valued pictures, which have extensive applications in both civil and military domains. Among these applications, SAR electronic countermeasures currently represent a prominent area of research interest. Presently, within the radar electronic countermeasures, there exists a diminishing disparity among the features of real and false targets, rendering the detection of jamming increasingly challenging. This paper examines the phase of SAR images and presents a method for identifying SAR jamming regions based on phase features. The initial step involves organizing the cluttered phase information into neighborhood phase differences. Subsequently, this information is coupled with the amplitude to obtain the weighted phase difference. This metric effectively captures the extent of phase distortion resulting from jamming. The findings from the simulation experiment demonstrate that the proposed feature and method are capable of accurately identifying and filtering out the jamming region in SAR pictures. Furthermore, it demonstrates the prospection of phase within the SAR image interpretation and electronic countermeasures.
{"title":"Jamming detection based on phase feature for SAR images","authors":"Haoyu Zhang, Sinong Quan, Shiqi Xing, Yitao Liu","doi":"10.1117/12.3014617","DOIUrl":"https://doi.org/10.1117/12.3014617","url":null,"abstract":"Synthetic Aperture Radar (SAR) is capable of producing high-resolution complex-valued pictures, which have extensive applications in both civil and military domains. Among these applications, SAR electronic countermeasures currently represent a prominent area of research interest. Presently, within the radar electronic countermeasures, there exists a diminishing disparity among the features of real and false targets, rendering the detection of jamming increasingly challenging. This paper examines the phase of SAR images and presents a method for identifying SAR jamming regions based on phase features. The initial step involves organizing the cluttered phase information into neighborhood phase differences. Subsequently, this information is coupled with the amplitude to obtain the weighted phase difference. This metric effectively captures the extent of phase distortion resulting from jamming. The findings from the simulation experiment demonstrate that the proposed feature and method are capable of accurately identifying and filtering out the jamming region in SAR pictures. Furthermore, it demonstrates the prospection of phase within the SAR image interpretation and electronic countermeasures.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the color model, this paper investigates the green glass in Cantonese colored windows, aiming to establish the standard value of the green color of Cantonese colored windows, and to provide relevant data and suggestions to protect the design concept of Cantonese architectural decoration and regulate the use of color in colored windows. By collecting samples, with the help of image processing and color analysis software, the green glass was positioned and analyzed in both RGB and LAB color models. It is found that in the RGB color model, the threshold intervals of green glass are mainly concentrated in the Forest Green and Green regions; in the LAB color model, the threshold intervals of the two-color channels of green glass are mainly distributed in the range of medium and low saturation. This study provides digitalized standard values and reference data for the green of the Cantonese colored windows, which helps to maintain the design style of traditional architecture and promote the development and application of colored windows.
{"title":"Research on Green glass of Cantonese colored windows based on color model","authors":"Xiaoqing Wang, Ying Du","doi":"10.1117/12.3014495","DOIUrl":"https://doi.org/10.1117/12.3014495","url":null,"abstract":"Based on the color model, this paper investigates the green glass in Cantonese colored windows, aiming to establish the standard value of the green color of Cantonese colored windows, and to provide relevant data and suggestions to protect the design concept of Cantonese architectural decoration and regulate the use of color in colored windows. By collecting samples, with the help of image processing and color analysis software, the green glass was positioned and analyzed in both RGB and LAB color models. It is found that in the RGB color model, the threshold intervals of green glass are mainly concentrated in the Forest Green and Green regions; in the LAB color model, the threshold intervals of the two-color channels of green glass are mainly distributed in the range of medium and low saturation. This study provides digitalized standard values and reference data for the green of the Cantonese colored windows, which helps to maintain the design style of traditional architecture and promote the development and application of colored windows.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on 3D images, this study aims to explore automatic segmentation and enhancement methods for airfield runway surface cracks. Firstly, a typical 2D Gaussian filter is used to remove noise from the road surface data. Then, Steerable Matched Filter (SMFB) is introduced to extract crack features. By constructing a set of 52 SMFB filters with different parameters, we are able to accurately capture cracks with different directions and sizes. After that, Tensor Voting (TV) technique is introduced to further enhance the continuity of the cracks. With this method, we are able to detect and segment the cracks in the airfield runway surface for a more accurate and comprehensive analysis. The experimental results show that the proposed method performs well in crack detection and segmentation, providing strong support for airport pavement maintenance and management.
{"title":"The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniques","authors":"Shanshan Zhai, Yanna Xu","doi":"10.1117/12.3014473","DOIUrl":"https://doi.org/10.1117/12.3014473","url":null,"abstract":"Based on 3D images, this study aims to explore automatic segmentation and enhancement methods for airfield runway surface cracks. Firstly, a typical 2D Gaussian filter is used to remove noise from the road surface data. Then, Steerable Matched Filter (SMFB) is introduced to extract crack features. By constructing a set of 52 SMFB filters with different parameters, we are able to accurately capture cracks with different directions and sizes. After that, Tensor Voting (TV) technique is introduced to further enhance the continuity of the cracks. With this method, we are able to detect and segment the cracks in the airfield runway surface for a more accurate and comprehensive analysis. The experimental results show that the proposed method performs well in crack detection and segmentation, providing strong support for airport pavement maintenance and management.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To achieve automatic fruit object recognition in complex backgrounds, this paper proposes a fruit object detection algorithm based on YOLO-GF. Addressing challenges such as complex backgrounds, significant variations in target shapes, and instances of occlusion in fruit images, we utilize the Global Attention Mechanism (GAM) to enhance the feature extraction capability for fruit targets, thereby improving fruit recognition accuracy. Additionally, the Focal-EIOU loss function is used instead of the CIOU loss function to expedite model convergence. Experimental results demonstrate a significant improvement in recognition accuracy under the same hardware conditions. On the same test dataset, the improved model achieves an mAP50 of 92.1% and mAP50:95 of 76.5%, representing increases of 5.8% and 11.9% compared to the original model, respectively.
{"title":"A deep learning approach for fruit detection: YOLO-GF","authors":"J. Guo, Wei Wu","doi":"10.1117/12.3014430","DOIUrl":"https://doi.org/10.1117/12.3014430","url":null,"abstract":"To achieve automatic fruit object recognition in complex backgrounds, this paper proposes a fruit object detection algorithm based on YOLO-GF. Addressing challenges such as complex backgrounds, significant variations in target shapes, and instances of occlusion in fruit images, we utilize the Global Attention Mechanism (GAM) to enhance the feature extraction capability for fruit targets, thereby improving fruit recognition accuracy. Additionally, the Focal-EIOU loss function is used instead of the CIOU loss function to expedite model convergence. Experimental results demonstrate a significant improvement in recognition accuracy under the same hardware conditions. On the same test dataset, the improved model achieves an mAP50 of 92.1% and mAP50:95 of 76.5%, representing increases of 5.8% and 11.9% compared to the original model, respectively.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianjun Peng, Jialei Zhai, Xiang Jin, Chengshuang Hu, Zaigang Li
As the global pandemic gradually eases and the aviation transport industry continues to experience steady growth, highdensity flight operations are becoming the new normal. The intelligentization of flight support processes is a crucial avenue for enhancing both the safety and efficiency of flight operations. With the advancement of computer vision technology, video-based object tracking has shown significant potential in the context of flight support processes. However, in real airport environments, object tracking often encounters challenges such as occlusion, scale variations, rotation, and changes in lighting conditions, leading to a decrease in tracking accuracy and even target loss. In this paper, our focus is on overcoming tracking failures caused by occlusion, deformation, and lighting variations. We have conducted the following work, taking into consideration the unique characteristics of airport environments and the specific requirements of flight support processes: (i) We utilized features at three levels, namely, Histogram of Oriented Gradient (HOG), Color Names, and Convolutional Neural Networks (CNN), to describe the texture, color, and high-level semantics of video images, respectively. (ii) We employed a multi-feature fusion approach using a trilinear interpolation function to integrate information from various sources. (iii) We implemented improved ECO algorithms for the tracking of moving objects in the airport environment. Finally, we validated this object tracking system using real surveillance videos from the airport. Experimental results have demonstrated the effectiveness and practicality of the method under challenging conditions.
{"title":"The application of target tracking algorithm in intelligent video system to flight support","authors":"Jianjun Peng, Jialei Zhai, Xiang Jin, Chengshuang Hu, Zaigang Li","doi":"10.1117/12.3014375","DOIUrl":"https://doi.org/10.1117/12.3014375","url":null,"abstract":"As the global pandemic gradually eases and the aviation transport industry continues to experience steady growth, highdensity flight operations are becoming the new normal. The intelligentization of flight support processes is a crucial avenue for enhancing both the safety and efficiency of flight operations. With the advancement of computer vision technology, video-based object tracking has shown significant potential in the context of flight support processes. However, in real airport environments, object tracking often encounters challenges such as occlusion, scale variations, rotation, and changes in lighting conditions, leading to a decrease in tracking accuracy and even target loss. In this paper, our focus is on overcoming tracking failures caused by occlusion, deformation, and lighting variations. We have conducted the following work, taking into consideration the unique characteristics of airport environments and the specific requirements of flight support processes: (i) We utilized features at three levels, namely, Histogram of Oriented Gradient (HOG), Color Names, and Convolutional Neural Networks (CNN), to describe the texture, color, and high-level semantics of video images, respectively. (ii) We employed a multi-feature fusion approach using a trilinear interpolation function to integrate information from various sources. (iii) We implemented improved ECO algorithms for the tracking of moving objects in the airport environment. Finally, we validated this object tracking system using real surveillance videos from the airport. Experimental results have demonstrated the effectiveness and practicality of the method under challenging conditions.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the current problem of unsatisfactory vehicle detection in complex scenes, an improved vehicle target detection network model is proposed. First, Res2Net residual network is fused in SCP, and the CSP_R structure is proposed, so that the model can extract deeper feature information and strengthen the ability to characterize small-scale targets; the attention mechanism is introduced, and the C3_CBAM module is designed to strengthen the attention to the detection targets while avoiding the increase of the model's computational volume; the loss function of the MPDIoU regression optimization is introduced, and the loss function is optimized by combining the prediction frame with the real frame length, width and area loss, and quantitative indicators to improve the convergence speed and robustness of the model. Finally, the model is validated on the SODA10M dataset, and the experimental results show that the model detection speed reaches 32 frames per second. The average detection accuracy reaches 83.7%, which is an improvement of 7.8 percentage points compared with YOLOV5s.
{"title":"Research on target detection algorithm based on vehicle detection","authors":"Yanguo Huang, Zehao Rao, Luo Li","doi":"10.1117/12.3014382","DOIUrl":"https://doi.org/10.1117/12.3014382","url":null,"abstract":"Aiming at the current problem of unsatisfactory vehicle detection in complex scenes, an improved vehicle target detection network model is proposed. First, Res2Net residual network is fused in SCP, and the CSP_R structure is proposed, so that the model can extract deeper feature information and strengthen the ability to characterize small-scale targets; the attention mechanism is introduced, and the C3_CBAM module is designed to strengthen the attention to the detection targets while avoiding the increase of the model's computational volume; the loss function of the MPDIoU regression optimization is introduced, and the loss function is optimized by combining the prediction frame with the real frame length, width and area loss, and quantitative indicators to improve the convergence speed and robustness of the model. Finally, the model is validated on the SODA10M dataset, and the experimental results show that the model detection speed reaches 32 frames per second. The average detection accuracy reaches 83.7%, which is an improvement of 7.8 percentage points compared with YOLOV5s.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shi Wang, Xiangju Liu, Xinshu Liu, JiaHui Chen, XiaoHong Wang
The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching.
行人多目标跟踪技术的主要任务是在视频序列中同时连续跟踪多个行人目标，并保持其唯一的 ID 编号。然而，目前的行人多目标跟踪模型仍然存在很多问题，例如误检、漏检，以及当行人受到遮挡或外观过于相似时频繁切换 ID 号，最终导致跟踪失败。因此，本文提出了一种基于 TBD 策略的行人多目标跟踪模型。它主要由两部分组成：行人检测器和行人跟踪器。在行人检测器方面，本文使用 ES-YOLO 行人检测器。在行人跟踪器方面，本文借鉴 OSNet 中的全尺度特征学习模块，重新设计了 StrongSORT 行人外观特征提取网络，最终得到了基于全尺度特征融合的 StrongSORT 行人跟踪器，进一步增强了其行人特征提取能力。在实验结果方面。基于本文 TBD 策略的行人多目标跟踪模型在 MOT16 数据集上的实验结果表明，本文提出的行人多目标跟踪模型能有效提高行人多目标跟踪的精度，减少行人 ID 号频繁切换的问题。
{"title":"Optimization research on pedestrian multiobjects tracking model based on TBD strategy","authors":"Shi Wang, Xiangju Liu, Xinshu Liu, JiaHui Chen, XiaoHong Wang","doi":"10.1117/12.3014360","DOIUrl":"https://doi.org/10.1117/12.3014360","url":null,"abstract":"The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the wide application of 3D building cluster models in urban planning, visualization and other fields, how to improve the rendering efficiency and reduce the computational cost of building cluster models has become an important issue. To address this problem, this paper proposes a visual perception evaluation model used to assess the weights of buildings based on multi-factor considerations to determine the order of building simplification, and weights the vertex importance for the classical QEM algorithm to redefine the collapsing cost of the edges, which achieves the purpose of reducing the complexity of the model while maintaining the visual quality. Experimental results show that the algorithm can significantly reduce the model rendering time and computational cost while maintaining the visual quality.
{"title":"Research on the simplification of building complex model under multi-factor constraints","authors":"Haoyuan Bai, Kelong Yang, Shunhua Liao","doi":"10.1117/12.3014388","DOIUrl":"https://doi.org/10.1117/12.3014388","url":null,"abstract":"With the wide application of 3D building cluster models in urban planning, visualization and other fields, how to improve the rendering efficiency and reduce the computational cost of building cluster models has become an important issue. To address this problem, this paper proposes a visual perception evaluation model used to assess the weights of buildings based on multi-factor considerations to determine the order of building simplification, and weights the vertex importance for the classical QEM algorithm to redefine the collapsing cost of the edges, which achieves the purpose of reducing the complexity of the model while maintaining the visual quality. Experimental results show that the algorithm can significantly reduce the model rendering time and computational cost while maintaining the visual quality.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the difficulty of point feature matching in 3D reconstruction to meet the tracking requirements of weakly textured scenes, this paper proposes a visual SLAM algorithm based on grid method combining points with edge features. In the tracking thread, a method based on grid method is proposed to evaluate the feature quality of points. The textures of external environment are judged according to ORB feature description, and the information of Canny edge features of weakly textured mesh is added to improve the positioning accuracy. In the local mapping thread, the joint feature points pose and map points are iteratively optimized to improve the convergence rate of the algorithm. The simulation results show that the proposed algorithm has a good location and tracking effects in the weak texture scene.
针对三维重建中点特征匹配难以满足弱纹理场景跟踪要求的问题，本文提出了一种基于网格法的视觉 SLAM 算法，将点与边缘特征相结合。在跟踪线程中，提出了一种基于网格法的点特征质量评估方法。根据 ORB 特征描述对外部环境的纹理进行判断，并加入弱纹理网格的 Canny 边缘特征信息，以提高定位精度。在局部映射线程中，对联合特征点姿态和映射点进行迭代优化，以提高算法的收敛速度。仿真结果表明，所提出的算法在弱纹理场景中具有良好的定位和跟踪效果。
{"title":"RGB-D visual SLAM for point association local edge features","authors":"Hongtu Li, Fang Wang, Yunjiang Zhang","doi":"10.1117/12.3014358","DOIUrl":"https://doi.org/10.1117/12.3014358","url":null,"abstract":"Aiming at the difficulty of point feature matching in 3D reconstruction to meet the tracking requirements of weakly textured scenes, this paper proposes a visual SLAM algorithm based on grid method combining points with edge features. In the tracking thread, a method based on grid method is proposed to evaluate the feature quality of points. The textures of external environment are judged according to ORB feature description, and the information of Canny edge features of weakly textured mesh is added to improve the positioning accuracy. In the local mapping thread, the joint feature points pose and map points are iteratively optimized to improve the convergence rate of the algorithm. The simulation results show that the proposed algorithm has a good location and tracking effects in the weak texture scene.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A detection algorithm of maximum and minimum eigenvalues based on random matrix theory is proposed for the problem of abnormal detection of customer electricity consumption. Firstly, the data source matrix is constructed by time alignment and superimposed Gaussian white noise, and the sliding window method is used to obtain the window data indicating the operation status at each moment; secondly, the window data are standardized, feature extraction and other operations are performed, and the difference and the sum of the maximum and minimum eigenvalues are compared to construct the feature detection indexes and thresholds; finally, the algorithm is studied and verified by simulation. The results show that the algorithm does not depend on any model, can analyze the operation status of the system more comprehensively and adequately, and realizes the effective detection of abnormal data
{"title":"Identification of customer electricity usage anomalies based on random matrix theory","authors":"Shuo Zhou, Qihui Wang","doi":"10.1117/12.3014405","DOIUrl":"https://doi.org/10.1117/12.3014405","url":null,"abstract":"A detection algorithm of maximum and minimum eigenvalues based on random matrix theory is proposed for the problem of abnormal detection of customer electricity consumption. Firstly, the data source matrix is constructed by time alignment and superimposed Gaussian white noise, and the sliding window method is used to obtain the window data indicating the operation status at each moment; secondly, the window data are standardized, feature extraction and other operations are performed, and the difference and the sum of the maximum and minimum eigenvalues are compared to construct the feature detection indexes and thresholds; finally, the algorithm is studied and verified by simulation. The results show that the algorithm does not depend on any model, can analyze the operation status of the system more comprehensively and adequately, and realizes the effective detection of abnormal data","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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