| ［1］ |
Rodriguez L A F,Uribe J A, Bonilla J F V.Obstacle detection over rails using hough transform［C］∥Proc of 2012 XVII Symposium of Image,Signal Processing, and Artificial Vision(STSIVA),2012:317-322.
|
| ［2］ |
Gibert X,Patel V M, Chellappa R.Deep multi-task learning for railway track inspection［J］.IEEE Transactions on Intelligent Transportation Systems,2015,18(1):153-164.
|
| ［3］ |
Xu X, Lie Y, Yang F.Railway subgrade defect
|
|
automatic recognition method based on improved Faster R-CNN［J］.Scientific Programming,2018(6):1-12.
|
| ［4］ |
Santur Y,Karakose M,Akin E.A new rail inspection method based on deep learning using laser cameras［C］∥Proc of IEEE International Artificial Intelligence & Data Processing Symposium,2017:1-6.
|
| ［5］ |
Zhang X,Wang K, Wang Y,et al.An improved method of rail health monitoring based on CNN and multiple acoustic emission events［C］∥Proc of 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC),2017:1-6.
|
| ［6］ |
Makantasis K, Protopapadakis E, Doulamis A,et al. Deep convolutional neural networks for efficient vision based tunnel inspection［C］∥Proc of IEEE International Conference on Intelligent Computer Communication & Processing,2015:335-342.
|
| ［7］ |
Protopapadakis E,Stentoumis C, Doulamis N,et al.Autonomous roboitic inspection in tunnels［J］.
|
|
International Society for Photogrammetry and Remote Sensing, 2016,111-5:167-174.
|
| ［8］ |
Krizhevsky A,Sutskever I,Hinton G.ImageNet classification with deep convolutional neural networks［C］∥Proc of NIPS,2012:1097-1105.
|
| ［9］ |
Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition［J］. arXiv:1409.1556,2014.
|
| ［10］ |
Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions［C］∥Proc of 2015 IEEE Conference on Computer Vision and Pattern Recognition,2015:1-9.
|
| ［11］ |
He K, Zhang X, Ren S,et al.Deep residual learning for image recognition［C］∥Proc of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016: 770-778.
|
| ［12］ |
Iandola F N,Han S,Moskewicz M W,et al.SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size［J］.arXiv:1602.07360,2016.
|
| ［13］ |
Howard A G,Zhu M, Chen B,et al.MobileNets: Efficient convolutional neural networks for mobile vision applications［J］.arXiv:1704.04861,2017.
|
| ［14］ |
Sandler M,Howard A,Zhu M,et al.Mobilenetv2: Inverted residuals and linear bottlenecks［C］∥Proc of 2018 IEEE Conference on Computer Vision and Pattern Recognition,2018: 4510-4520.
|
| ［15］ |
Zhang X,Zhou X,Lin M,et al.ShuffleNet: An extremely efficient convolutional neural network for mobile devices［C］∥
|
|
Proc of 2018 IEEE Conference on Computer Vision and Pattern Recognition,2018:6848-6856.
|
| ［16］ |
Perez L,Wang J.The effectiveness of data augmentation in image classification using deep learning［J］.arXiv:1712.04621,2017.
|
| ［17］ |
Lin M, Chen Q, Yan S.Network in network［J］.arXiv:1312.4400,2013.
|
