计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 319-324.doi: 10.11896/jsjkx.210500124
王君锋1,2, 刘凡1,2, 杨赛3, 吕坦悦1,2, 陈峙宇1,2, 许峰2
WANG Jun-feng1,2, LIU Fan1,2, YANG Sai3, LYU Tan-yue1,2, CHEN Zhi-yu1,2, XU Feng2
摘要： 针对现有深度学习方法在进行大坝裂缝检测时出现模型过拟合、计算效率低下等问题,文中提出了一种基于多源迁移学习的大坝裂缝检测方法,旨在提高算法准确率的同时,减少模型计算量,加快检测速度。所提方法首先将MobileNet网络和SSD目标检测算法相结合,形成MobileNet-SSD网络,有效减少了模型参数量并减少了计算复杂度;然后利用道路裂缝、墙壁裂缝和桥梁裂缝等多源数据进行训练,并应用迁移学习的思想,将学习到的知识分别迁移到大坝裂缝的检测模型中,以提升模型检测的精确度;最后提出了一种多模型融合方法,将通过迁移学习得到的多个检测结果进行融合,进一步提升了检测结果的重合度。
中图分类号:
| [1] DONG L B.Analysis and study on the influencing factors of dam safety [D].Nanjing:Hohai University,2008. [2] YU J Y,DING P C,WANG C.Overview:Application of Convolution Neural Network in Object Detection[J].Computer Science,2018,45(S2):17-26. [3] LIENHART R,MAYDT J.An extended set of Haar-like fea-tures for rapid object detection[C]//International Conference on Image Processing.New York:IEEE Press,2002:900-903. [4] VIOLA P,JONES M.Rapid Object Detection using Cascade of Simple Features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR).New York:IEEE Press,2003:511-518. [5] FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,32(9):1627-1645. [6] LI Z W,LIU F,YANG W J,et al.A Survey of ConvolutionalNeural Networks:Analysis,Applications,and Prospects[J].IEEE Transactions on Neural Networks and Learning Systems,2021,2021(99):1-21. [7] GIRSHICK R,DONAHUE J,DARRELI T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2014:580-587. [8] KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[J].Communications of the ACM,2017,60(6):84-90. [9] GIRSHICK R.Fast R-CNN[C]//IEEE International Confe-rence on Computer Vision(ICCV).New York:IEEE Press,2015:1440-1448. [10] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.New York:IEEE Press,2017,39(6):1137-1149. [11] REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]//IEEE Confe-rence on Computer Vision and Pattern Recognition(CVPR).New York:IEEE Press,2016:779-788. [12] LIU W,ANUUELOV D,ERHAN D,et al.SSD:Single ShotMultiBox Detector[C]//Computer Vision-14th European Conference(ECCV 2016).New York:IEEE Press,2016,9905:21-37. [13] CHEN W,FAN X N,LI M,et al.Dam Crack Detection of Artificial Colony Algorithm Based on Gabor Operator[J].Microprocessors,2015,36(4):32-38. [14] ZHANG D W,XU M Z,MA L,et al.Research on image segmentation method of underwater dam cracks[J].Software Guide,2016,15(9):170-172. [15] XU H,HE J J,FANG L.Dam Crack Detection Based on Image Saliency[J].Industrial Control Computer,2018,31(9):19-20,23. [16] VALENCA J,JULIO E.MCrack-Dam:the scale-up of a method to assess cracks on concrete dams by image processing.The case study of Itaipu Dam,at the Brazil-Paraguay border[J].Journal of Civil Structural Health Monitoring,2018,2018(8):857-866. [17] FAN X N,WU J J,SHI P F,et al.A Novel Underwater Dam Crack Extraction Algorithm Based on Lorentz Information Measure[J].Computer and Modernization,2018(3):73-77. [18] JIANG X Y,ZAHNG S,HE S Y.Dam crack detection based on lattice Boltzmann model[J].Science & Technology View,2018,233(11):98-99. [19] MAO Y C,QI H,CHEN H,et al.A comprehensive evaluation method for dam safety based on deep learning:China,201710598587[P].2017-4. [20] MAO Y G,WANG J,CHEN X L,et al.Dam Defect Recognition and Classification Based on Feature Combination and CNN[J].Computer Science,2019,46(3):273-280. [21] WANG S,ZHANG H,WANG H,et al.Combination of Point-Cloud Model and FCN for Dam Crack Detection and Scale Calculation[C]//2019 Chinese Automation Congress(CAC).New York:IEEE Press,2019:5859-5862. [22] TANG J,MAO Y,WANG J,et al.Multi-task Enhanced Dam Crack Image Detection Based on Faster R-CNN[C]//IEEE 4th International Conference on Image,Vision and Computing(ICIVC).New York:IEEE Press,2019:336-340. [23] LI L J,ZHANG H,PANG J,et al.Dam surface crack detection based on deep learning[C]//2019 International Conference on Robotics,Intelligent Control and Artificial Intelligence(RICAI).New York:ACM,2019:738-743. [24] FAN X,WU J,SHI P,et al.A novel automatic dam crack detection algorithm based on local-global clustering[J].Multimedia Tools & Applications,2018(2018):26581-26599. [25] SOUSA M J,MOUTINHO A,ALMEIDA M.Wildfire detection using transfer learning on augmented datasets[J].Expert Systems with Applications,2020,142(2020):1-14. [26] LI X D,HU Y,LI M T,et al.Fault diagnostics between diffe-rent type of components:A transfer learning approach[J].Applied Soft Computing,2020,86(2020):1-11. [27] ZHENG Z,QI H,ZHUANG L,et al.Automated Rail SurfaceCrack Analytics Using Deep Data-driven Models and Transfer Learning[J].Sustainable Cities and Society,2021,70(2021):1-11. [28] HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv:1704.04861,2017. [29] AKCAY S,KUNDEGORSKI M E,WILLCOCKS C G,et al.Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Secu-rity Imagery[J].IEEE Transactions on Information Forensics & Security,2018,13(9):2203-2215. [30] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Net-works for Large-Scale Image Recognition[C]//ICLR 2015.New York:IEEE Press,2014:1-14. [31] KE Y,LU Y L.Pet dog recognition method based on VGG16[J].Practical Electronics,2020,407(21):44-47. [32] REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].arXiv,2018:1-6. [33] NIE M,WANG C.Pavement Crack Detection based on yolo v3[C]//2019 2nd International Conference on Safety Produce Informatization(IICSPI).New York:IEEE Press,2019:327-330. [34] DARSH P,RATHI P,KUMAR M.YOLO v3-Tiny:Object Detection and Recognition using one stage improved model[C]//2020 6th International Conference on Advanced Computing and Communication Systems(ICACCS).New York:IEEE Press,2020:687-694. [35] TANG J,MAO Y,WANG J,et al.Multi-task Enhanced Dam Crack Image Detection Based on Faster R-CNN[C]//2019 IEEE 4th International Conference on Image,Vision and Computing(ICIVC).New York:IEEE Press,2019:336-340. |
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