摘要
大坝缺陷识别分类技术是人类智能的基本表现,它是最典型、最困难的模式识别问题之一。由于大坝缺陷图像具有信噪比低、光照分布极度不均匀等特征,分类识别算法的识别率较低。针对这些问题,文中提出一种基于图像LBP特征和Gabor特征组合与CNN相结合(LBP and Gabor feature combination and CNN,LG-CNN)的缺陷图像识别方法,对采集到的大坝图像进行分析,实现对缺陷图像的识别和分类。该方法首先分别提取图像的LBP特征与Gabor特征;然后将得到的LBP特征和Gabor特征组合作为CNN的输入;最后通过逐层训练网络,实现大坝缺陷类型的分类识别。实验结果表明,LG-CNN的平均识别准确率为88.39%,缺陷召回率为92.75%,与相同参数设置下的CNN分类识别算法相比,识别准确率和缺陷召回率分别约提高了3.1%和2.5%,具有最优的结果。
Dam defect recognition and classification technology is the basic manifestation of human intelligence.It is one of the most typical and difficult pattern recognition problems.Due to the low signal-to-noise ratio and extremely uneven illumination distribution of dam defects,the recognition rate of classification and recognition algorithm is relatively low.In order to solve these problems,this paper proposed a defect image recognition method based on the combination of ima- ge LBP features and image Gabor features combined with CNN(LBP and Gabor feature combination and CNN,LG-CNN),analyzed the collected dam image,and realized the recognition and classification of the defective images.Firstly,the LBP features and the Gabor features of images are extracted respectively.Then,the features of LBP and Gabor are combined to be the input of CNN.Finally,by training the network layer by layer,the classification and recognition of dam defects are realized.The experimental results show that the average recognition accuracy of LG -CNN is 88.39%,as well as the recall rate of defect is 92.75%.Compared with the CNN classification algorithm under the same parameters,the recognition accuracy and the recall rate of defect are increased by 3.1% and 2.5% respectively,and the results is the best results.
作者
毛莺池
王静
陈小丽
徐淑芳
陈豪
MAO Ying-chi;WANG Jing;CHEN Xiao-li;XU Shu-fang;CHEN Hao(College of Computer and Information,Hohai University,Nanjing 211100,China;College of Water Conservancy and Hydropower,Hohai University,Nanjing 210098,China)
出处
《计算机科学》
CSCD
北大核心
2019年第3期267-274,共8页
Computer Science
基金
"十三五"国家重点研发计划项目(2018YFC0407105)
华能集团重点研发课题(HNKJ17-21)资助
