{"title":"脉冲耦合神经网络与交叉熵算法相结合的高速公路裂缝分割算法研究","authors":"Baocheng Wang, Jiawei Sun","doi":"10.12783/ISSN.1544-8053/14/S1/6","DOIUrl":null,"url":null,"abstract":"The expressway crack identification is significantly important for the expressway safety maintenance, and the crack detection is one of key technologies for the crack identification. This paper proposed an expressway crack detection method based on improved Pulse-Coupled Neural Network (PCNN), used minimum cross-entropy algorithm to obtain the optimal iterations of PCNN algorithm, and then complete the segmentation of expressway images by combining the simplified PCNN algorithm. The results showed that this method could inhibit the background noise and better extract continuous crack edge to provide good characteristics for crack identification in the next step.","PeriodicalId":17101,"journal":{"name":"Journal of Residuals Science & Technology","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Expressway Crack Segmentation Algorithm Combined with Pulse-Coupled Neural Network and Cross-Entropy Algorithm\",\"authors\":\"Baocheng Wang, Jiawei Sun\",\"doi\":\"10.12783/ISSN.1544-8053/14/S1/6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The expressway crack identification is significantly important for the expressway safety maintenance, and the crack detection is one of key technologies for the crack identification. This paper proposed an expressway crack detection method based on improved Pulse-Coupled Neural Network (PCNN), used minimum cross-entropy algorithm to obtain the optimal iterations of PCNN algorithm, and then complete the segmentation of expressway images by combining the simplified PCNN algorithm. The results showed that this method could inhibit the background noise and better extract continuous crack edge to provide good characteristics for crack identification in the next step.\",\"PeriodicalId\":17101,\"journal\":{\"name\":\"Journal of Residuals Science & Technology\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Residuals Science & Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12783/ISSN.1544-8053/14/S1/6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Residuals Science & Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12783/ISSN.1544-8053/14/S1/6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Expressway Crack Segmentation Algorithm Combined with Pulse-Coupled Neural Network and Cross-Entropy Algorithm
The expressway crack identification is significantly important for the expressway safety maintenance, and the crack detection is one of key technologies for the crack identification. This paper proposed an expressway crack detection method based on improved Pulse-Coupled Neural Network (PCNN), used minimum cross-entropy algorithm to obtain the optimal iterations of PCNN algorithm, and then complete the segmentation of expressway images by combining the simplified PCNN algorithm. The results showed that this method could inhibit the background noise and better extract continuous crack edge to provide good characteristics for crack identification in the next step.
期刊介绍：
The international Journal of Residuals Science & Technology (JRST) is a blind-refereed quarterly devoted to conscientious analysis and commentary regarding significant environmental sciences-oriented research and technical management of residuals in the environment. The journal provides a forum for scientific investigations addressing contamination within environmental media of air, water, soil, and biota and also offers studies exploring source, fate, transport, and ecological effects of environmental contamination.
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