{"title":"利用语义分割和复杂性追求算法进行复杂背景分割,以估算非接触式电缆振动频率","authors":"Tianyong Jiang, Chunjun Hu, Lingyun Li","doi":"10.1007/s13349-024-00798-6","DOIUrl":null,"url":null,"abstract":"<p>This paper proposes a new complex background segmentation method based on the modified fully convolutional network semantic segmentation for noncontact cable vibration frequency estimation. The estimation of frequency from video data is challenged by the presence of background object motion, which directly impacts the accuracy of the video-based method. To address this issue, image tests were carried out among the existing model (U2-Net) to explore the effect of the efficient channel attention (ECA) and convolutional block attention module (CBAM) on cable segmentation performance. As a result, a relative optimal model was identified. This modified model was then used to remove the complex background, while retaining the vibration signals specific to the cable. Subsequently, phase matrices encoding cable vibration were calculated using a phase-based motion estimation algorithm at various cable locations. The modal response of the cable vibration was estimated using the complexity pursuit (CP) algorithm from the segmented video. Finally, the vibration frequency of the cable was estimated. The proposed method was validated on a small-scale cable model. The results are in good agreement with the values sampled by the accelerometer, with an average relative error of 4.50%. This estimation shows the significant potential of the proposed method in structural health monitoring.</p>","PeriodicalId":48582,"journal":{"name":"Journal of Civil Structural Health Monitoring","volume":"39 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Complex background segmentation for noncontact cable vibration frequency estimation using semantic segmentation and complexity pursuit algorithm\",\"authors\":\"Tianyong Jiang, Chunjun Hu, Lingyun Li\",\"doi\":\"10.1007/s13349-024-00798-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper proposes a new complex background segmentation method based on the modified fully convolutional network semantic segmentation for noncontact cable vibration frequency estimation. The estimation of frequency from video data is challenged by the presence of background object motion, which directly impacts the accuracy of the video-based method. To address this issue, image tests were carried out among the existing model (U2-Net) to explore the effect of the efficient channel attention (ECA) and convolutional block attention module (CBAM) on cable segmentation performance. As a result, a relative optimal model was identified. This modified model was then used to remove the complex background, while retaining the vibration signals specific to the cable. Subsequently, phase matrices encoding cable vibration were calculated using a phase-based motion estimation algorithm at various cable locations. The modal response of the cable vibration was estimated using the complexity pursuit (CP) algorithm from the segmented video. Finally, the vibration frequency of the cable was estimated. The proposed method was validated on a small-scale cable model. The results are in good agreement with the values sampled by the accelerometer, with an average relative error of 4.50%. This estimation shows the significant potential of the proposed method in structural health monitoring.</p>\",\"PeriodicalId\":48582,\"journal\":{\"name\":\"Journal of Civil Structural Health Monitoring\",\"volume\":\"39 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Civil Structural Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s13349-024-00798-6\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Civil Structural Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s13349-024-00798-6","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Complex background segmentation for noncontact cable vibration frequency estimation using semantic segmentation and complexity pursuit algorithm
This paper proposes a new complex background segmentation method based on the modified fully convolutional network semantic segmentation for noncontact cable vibration frequency estimation. The estimation of frequency from video data is challenged by the presence of background object motion, which directly impacts the accuracy of the video-based method. To address this issue, image tests were carried out among the existing model (U2-Net) to explore the effect of the efficient channel attention (ECA) and convolutional block attention module (CBAM) on cable segmentation performance. As a result, a relative optimal model was identified. This modified model was then used to remove the complex background, while retaining the vibration signals specific to the cable. Subsequently, phase matrices encoding cable vibration were calculated using a phase-based motion estimation algorithm at various cable locations. The modal response of the cable vibration was estimated using the complexity pursuit (CP) algorithm from the segmented video. Finally, the vibration frequency of the cable was estimated. The proposed method was validated on a small-scale cable model. The results are in good agreement with the values sampled by the accelerometer, with an average relative error of 4.50%. This estimation shows the significant potential of the proposed method in structural health monitoring.
期刊介绍:
The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems.
JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.