{"title":"基于神经网络的扇形扫描图像微小人造物体检测","authors":"S. Perry, L. Guan","doi":"10.1109/OCEANS.2001.968325","DOIUrl":null,"url":null,"abstract":"This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.","PeriodicalId":326183,"journal":{"name":"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Detection of small man-made objects in sector scan imagery using neural networks\",\"authors\":\"S. Perry, L. Guan\",\"doi\":\"10.1109/OCEANS.2001.968325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.\",\"PeriodicalId\":326183,\"journal\":{\"name\":\"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANS.2001.968325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANS.2001.968325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of small man-made objects in sector scan imagery using neural networks
This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.