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参考文献

[1] 郭继昌,李重仪,郭春乐,等.水下图像增强和复原方法研究进展[J].中国图象图形学报,2017,22(3):273-287.

[2] HE K,SUN J,TANG X.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,33(12):2341-2353

[3] DREWS P,NASCIMENTO E,MORAES F,et al.Transmission estimation in underwater single images[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2013:825-830.

[4] GALDRAN A,PARDO D,PICÓN A,et al.Automatic red-channel underwater image restoration[J].Journal of Visual Communication and Image Representation.2015,26:132-145.

[5] Li C Y,Guo J C,Cong R M,et al.Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J].IEEE Transactions on Image Processing,2016,25(12):5664-5677.

[6] UEKI Y,IKEHARA M.Underwater image enhancement based on the iteration of a generalization of dark channel prior[C]//2019 IEEE Visual Communications and Image Processing (VCIP).IEEE,2019:1-4.

[7] 林森,白莹,李文涛,等.基于修正模型与暗通道先验信息的水下图像复原[J].机器人,2020,42(4):427-435.

[8] WANG Y,ZHANG J,CAO Y,et al.A deep CNN method for underwater image enhancement[C]//2017 IEEE International Conference on Image Processing (ICIP).IEEE,2017:1382-1386.

[9] CAO K,PENG Y T,COSMAN P C.Underwater image restoration using deep networks to estimate background light and scene depth[C]//2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).IEEE,2018:1-4.

[10] WANG K,HU Y,CHEN J,et al.Underwater image restoration based on a parallel convolutional neural network[J].Remote Sensing,2019,11(13):1591.

[11] YANG S,CHEN Z,FENG Z,et al.Underwater image enhancement using scene depth-based adaptive background light estimation and dark channel prior algorithms[J].IEEE Access,2019,7:165318-165327.

[12] PEREZ J,ATTANASIO A C,NECHYPORENKO N,et al.A deep learning approach for underwater image enhancement[C]//International Work-Conference on the Interplay between Natural and Artificial Computation.Springer,Cham,2017:183-192.

[13] DING X,WANG Y,LIANG Z,et al.Towards underwater image enhancement using super-resolution convolutional neural networks[C]//International Conference on Internet Multimedia Computing and Service.Springer,Singapore,2017:479-486.

[14] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems,2014:2672-2680.

[15] KNYAZ V A,KNIAZ V V,REMONDINO F.Image-to-voxel model translation with conditional adversarial networks[C]//Proceedings of the European Conference on Computer Vision (ECCV) Workshops.2018.

[16] LI M,HUANG H,MA L,et al.Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:184-199.

[17] LI J,SKINNER K A,EUSTICE R M,et al.WaterGAN:unsupervised generative network to enable real-time color correction of monocular underwater images[J].IEEE Robotics and Automation Letters,2017,3(1):387-394.

[18] FABBRI C,JAHIDUL ISLAM M,SATTAR J.Enhancing underwater imagery using generative adversarial networks[C]//2018 IEEE International Conference on Robotics and Automation (ICRA).IEEE,2018:7159-7165.

[19] 李庆忠,白文秀,牛炯.基于改进CycleGAN的水下图像颜色校正与增强[J].自动化学报,2023,49(4):820-829.

[20] ISLAM M J,XIA Y,SATTAR J.Fast underwater image enhancement for improved visual perception[J].IEEE Robotics and Automation Letters,2020,5(2):3227-3234.

[21] DUDHANE A,HAMBARDE P,PATIL P W,et al.Deep underwater image restoration and beyond[J].IEEE Signal Processing Letters,2020,27:675-679.

[22] 雍子叶,郭继昌,李重仪.融入注意力机制的弱监督水下图像增强算法[J].浙江大学学报(工学版),2021,55(3):555-562.

[23] LI H Y,ZHUANG P X.DewaterNet:A fusion adversarial real underwater image enhancement network[J].Signal Processing:Image Communication,2021,95.s

[24] 陈耀丹,王连明.基于卷积神经网络的人脸识别方法[J].东北师大学报(自然科学版),2016,48(2):70-76.

[25] 何逸炜,张军平.步态识别的深度学习:综述[J].模式识别与人工智能,2018,31(5):442-452.

[26] 蓝海磊.人群计数算法综述[J].计算机产品与流通,2019(7):91,93.

[27] 顾文涛,俞兴伟,李毅,等.基于深度学习的安全帽检测监控研究[J].电力设备管理,2020(5):42-43+49.

[28] 张新钰,高洪波,赵建辉,等.基于深度学习的自动驾驶技术综述[J].清华大学学报(自然科学版),2018,58(4):438-444.

[29] 杜敬.基于深度学习的无人机遥感影像水体识别[J].江西科学,2017,35(1):158-161,170.

[30] WANG T,CHEN Y,QIAO M,et al.A fast and robust convolutional neural network-based defect detection model in product quality control[J].The International Journal of Advanced Manufacturing Technology,2018,94(9):3465-3471.

[31] 许雪,TANVIR A.基于Faster R-CNN的多目标检测研究[J].计算机与数字工程,2020,48(10):2393-2399.

[32] 徐耀建.基于深度学习的视频多目标行人检测与追踪[J].现代信息科技,2020,4(12):6-9.

[33] BRYS T,HARUTYUNYAN A,VRANCX P,et al.Multi-objectivization and ensembles of shapings in reinforcement learning[J].Neurocomputing,2017,263:48-59.

[34] GAO S H,TAN Y Q,CHENG M M,et al.Highly efficient salient object detection with 100k parameters[C]//European Conference on Computer Vision.Springer,Cham,2020:702-721.

[35] FAN D P,ZHAI Y,BORJI A,et al.BBS-Net:RGB-D salient object detection with a bifurcated backbone strategy network[C]//European Conference on Computer Vision.Springer,Cham,2020:275-292.

[36] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2014:580-587.

[37] HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE transactions on pattern analysis and machine intelligence,2015,37(9):1904-1916.

[38] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE international conference on computer vision.2015:1440-1448.

[39] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].arXiv preprint arXiv:1506.01497,2015.

[40] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2016:779-788.

[41] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//European conference on computer vision.Springer,Cham,2016:21-37.

[42] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2017:7263-7271.

[43] REDMON J,FARHADI A.Yolov3:An incremental improvement[J].arXiv preprint arXiv:1804.02767,2018.

[44] BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:optimal speed and accuracy of object detection[J].arXiv e-prints:10.48550/arXiv.2004.10934.

[45] WANG W,WANG M,LI H,et al.Pavement crack image acquisition methods and crack extraction algorithms:A review[J].Journal of Traffic and Transportation Engineering (English Edition),2019,6(6):535-556.

[46] CAO W,LIU Q,HE Z.Review of pavement defect detection methods[J].IEEE Access,2020,8:14531-14544.

[47] KANG D,BENIPAL S S,GOPAL D L,et al.Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning[J].Automation in Construction,2020,118:103291.

[48] DUNG C V.Autonomous concrete crack detection using deep fully convolutional neural network[J].Automation in Construction,2019,99:52-58.

[49] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2015:3431-3440.

[50] RONNEBERGER O,FISCHER P,BROX T.U-net:convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention.Springer,Cham,2015:234-241.

[51] BADRINARAYANAN V,KENDALL A,CIPOLLA R.Segnet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(12):2481-2495.

[52] HUYAN J,LI W,TIGHE S,et al.CrackU-net:a novel deep convolutional neural network for pixelwise pavement crack detection[J].Structural Control and Health Monitoring,2020,27(8):e2551.

[53] CHENG J,XIONG W,CHEN W,et al.Pixel-level crack detection using U-Net[C]//International Conference on Industrial Control and Electronics Engineering,2012.

[54] LI S,ZHAO X.Automatic crack detection and measurement of concrete structure using convolutional encoder-decoder network[J].IEEE Access,2020,8:134602-134618.

[55] ZOU Q,ZHANG Z,LI Q,et al.Deepcrack:Learning hierarchical convolutional features for crack detection[J].IEEE Transactions on Image Processing,2018,28(3):1498-1512.

[56] O'BYRNE M,PAKRASHI V,SCHOEFS F,et al.Semantic segmentation of underwater imagery using deep networks trained on synthetic imagery[J].Journal of Marine Science and Engineering,2018,6(3):93.

[57] FAN X,WU J,SHI P,et al.A novel automatic dam crack detection algorithm based on local-global clustering[J].Multimedia Tools and Applications,2018,77(20):26581-26599.

[58] 马金祥,范新南,吴志祥,等.暗通道先验的大坝水下裂缝图像增强算法[J].中国图象图形学报,2016,21(12):1574-1584.

[59] 陈文静.水下大坝裂缝图像检测方法的研究[D].郑州:华北水利水电大学,2019.

[60] CHEN C,WANG J,ZOU L,et al.A novel crack detection algorithm of underwater dam image[C]//2012 International Conference on Systems and Informatics (ICSAI2012).IEEE,2012:1825-1828.

[61] VAN ENGELEN J E,HOOS H H.A survey on semi-supervised learning[J].Machine Learning,2020,109(2):373-440.

[62] SUNG F,YANG Y,ZHANG L,et al.Learning to compare:Relation network for few-shot learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2018:1199-1208.

[63] TAN C,SUN F,KONG T,et al.A survey on deep transfer learning[C]//International conference on artificial neural networks.Springer,Cham,2018:270-279.

[64] PIRES DE LIMA R,MARFURT K.Convolutional neural network for remote-sensing scene classification:Transfer learning analysis[J].Remote Sensing,2020.DOI:10.3390/rs12010086.

[65] WURM M,STARK T,ZHU X X,et al.Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks[J].ISPRS journal of photogrammetry and remote sensing,2019,150:59-69. 4idnp/iNMvnxT66MFQKPQLHmep7kRyOKEgjepKbJ7Enefq6GIpKhIjDx5gzBpgwi

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