{"title":"海底电缆旋转不变声呐图像分割","authors":"Songbo Xu;He Shen;Yixin Yang","doi":"10.1109/JOE.2025.3557927","DOIUrl":null,"url":null,"abstract":"Undersea cable detection is a prerequisite for cable maintenance and repair. However, extracting cables from side-scan sonar images is challenging due to the lack of details and interference from seabed sediments. In this article, an automatic rotation-invariant segmentation method for undersea cables is proposed. First, a filter based on the curvelet transform is designed to extract features of cables automatically. Second, a 2-D constant false alarm rate detector is used for feature denoising. Third, a morphology repair method is proposed to fulfill features that have been missed during feature extraction and image denoising. Finally, the maximum connected area in images is retained for cable segmentation. Results show that the proposed method can extract cables accurately. Four performance indicators, including structural similarity index, precision, pixel accuracy, and intersection over union reach 0.9810, 0.6108, 0.8348, and 0.8915, respectively. Consistent performance has been observed in images with different cable postures.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2345-2354"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rotation Invariant Sonar Image Segmentation for Undersea Cables\",\"authors\":\"Songbo Xu;He Shen;Yixin Yang\",\"doi\":\"10.1109/JOE.2025.3557927\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Undersea cable detection is a prerequisite for cable maintenance and repair. However, extracting cables from side-scan sonar images is challenging due to the lack of details and interference from seabed sediments. In this article, an automatic rotation-invariant segmentation method for undersea cables is proposed. First, a filter based on the curvelet transform is designed to extract features of cables automatically. Second, a 2-D constant false alarm rate detector is used for feature denoising. Third, a morphology repair method is proposed to fulfill features that have been missed during feature extraction and image denoising. Finally, the maximum connected area in images is retained for cable segmentation. Results show that the proposed method can extract cables accurately. Four performance indicators, including structural similarity index, precision, pixel accuracy, and intersection over union reach 0.9810, 0.6108, 0.8348, and 0.8915, respectively. Consistent performance has been observed in images with different cable postures.\",\"PeriodicalId\":13191,\"journal\":{\"name\":\"IEEE Journal of Oceanic Engineering\",\"volume\":\"50 3\",\"pages\":\"2345-2354\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Oceanic Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11005641/\",\"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":"IEEE Journal of Oceanic Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11005641/","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}
Rotation Invariant Sonar Image Segmentation for Undersea Cables
Undersea cable detection is a prerequisite for cable maintenance and repair. However, extracting cables from side-scan sonar images is challenging due to the lack of details and interference from seabed sediments. In this article, an automatic rotation-invariant segmentation method for undersea cables is proposed. First, a filter based on the curvelet transform is designed to extract features of cables automatically. Second, a 2-D constant false alarm rate detector is used for feature denoising. Third, a morphology repair method is proposed to fulfill features that have been missed during feature extraction and image denoising. Finally, the maximum connected area in images is retained for cable segmentation. Results show that the proposed method can extract cables accurately. Four performance indicators, including structural similarity index, precision, pixel accuracy, and intersection over union reach 0.9810, 0.6108, 0.8348, and 0.8915, respectively. Consistent performance has been observed in images with different cable postures.
期刊介绍:
The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.