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Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies.
Neural networks in civil engineering: 1989–2000
Adeli, H. (2001) · 2000
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Bridging the gap–restoring and rebuilding the nation’s bridges
AAoSHaT, O. (2008) · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010) · 2010
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Object detection with discriminatively trained part-based models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010) · 2010
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Pavement pothole detection and severity measurement using laser imaging
Yu, X. and Salari, E. (2011) · 2011
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Concrete crack detection by multiple sequential image filtering
Nishikawa, T., Yoshida, J., Sugiyama, T., and Fujino, Y. (2012) · 2012
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013) · 2013
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Maintenance and repair guide book of the pavement 2013
JRA (2013) · 2013
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An effective surface inspection method of urban roads according to the pavement management situation of local governments
Kazuya, T., Akira, K., Shun, F., and Takeki, I. (2013) · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013) · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014) · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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Regionally enhanced multiphase segmentation technique for damaged surfaces
O’Byrne, M., Ghosh, B., Schoefs, F., and Pakrashi, V. (2014) · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Road crack detection using visual features extracted by gabor filters
Zalama, E., Gómez-García-Bermejo, J., Medina, R., and Llamas, J. (2014) · 2014
Cited alongside, same era.
Asphalt pavement crack detection using image processing and naïve bayes based machine learning approach
Chun, P.-j., Hashimoto, K., Kataoka, N., Kuramoto, N., and Ohga, M. (2015) · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2015) · 2015
Cited alongside, same era.
Fast r-cnn
Girshick, R. (2015) · 2015
Cited alongside, same era.
Mms(mobile measurement system)
KOKUSAI KOGYO CO., L. (2016) · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016) · 2016
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Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network
Maeda, H., Sekimoto, Y., and Seto, T. (2016) · 2016
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Present state and future of social capital aging. infrastructure maintenance information
MLIT (2016) · 2016
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016) · 2016
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Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., et al. (2015) · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Pothole detection system using a black-box camera
Jo, Y. and Ryu, S. (2015) · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J. (2015) · 2015
Cited alongside, same era.
A fast and adaptive road defect detection approach using computer vision with real time implementation
Akarsu, B., KARAKÖSE, M., PARLAK, K., Erhan, A., and SARIMADEN, A. (2016) · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., and Sun, J. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Redmon, J. and Farhadi, A. (2016) · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016) · 2016
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Road crack detection using deep convolutional neural network
Zhang, L., Yang, F., Zhang, Y. D., and Zhu, Y. J. (2016) · 2016
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Deep learning-based crack damage detection using convolutional neural networks
Cha, Y.-J., Choi, W., and Büyüköztürk, O. (2017) · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017) · 2017
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Crack detection using image processing: A critical review and analysis
Mohan, A. and Poobal, S. (2017) · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017) · 2017
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Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network
Zhang, A., Wang, K. C., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J. Q., and Chen, C. (2017) · 2017
Later among the works it cites.