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RMCRC-UTS Scholarship-2: Rail Infrastructure Defect Detection Through Video Analytics - student still to be confirmed.

Project Member(s): Zhang, J., Wu, Q.

Funding or Partner Organisation: Rail Manufacturing Cooperative Research Centre (RMCRC) (Railway Manufacturing Cooperative Research Centre Ltd)
Rail Manufacturing Cooperative Research Centre (RMCRC) (Railway Manufacturing Cooperative Research Centre Ltd)

Start year: 2017

Summary: This PhD project will target on defect detection through robust video analysis in a clutter environment. Compared with traditional infrastructure maintenance process, inspection through video analysis will significantly improve the working efficiency and eliminate the potential safety concern by reducing physical contact (inspection) between maintenance engineers and infrastructure facilities. This will reinforce the artificial intelligence based decision support for rail way maintenance.

Publications:

Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, H, Zhang, J, Zhang, J, Xu, J & Wu, Q 2021, 'Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification', IEEE Transactions on Multimedia, vol. 23, pp. 1666-1680.
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Huang, H, Zhang, J, Zhang, J, Wu, Q & Xu, C 1970, 'PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning', 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, ELECTR NETWORK, pp. 1602-1609.

Huang, H, Zhang, J, Zhang, J, Wu, Q & Xu, J 1970, 'Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning', 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Shanghai, China, pp. 91-96.
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Huang, H, Xu, J, Zhang, J, Wu, Q & Kirsch, C 1970, 'Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks', 2018 Digital Image Computing: Techniques and Applications (DICTA), 2018 Digital Image Computing: Techniques and Applications (DICTA), IEEE, Canberra, Australia.
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Guo, D, Xu, J, Zhang, J, Xu, M, Cui, Y & He, X 2017, 'User relationship strength modeling for friend recommendation on Instagram', Neurocomputing, vol. 239, pp. 9-18.
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Zhao, Y, Di, H, Zhang, J, Lu, Y, Lv, F & Li, Y 2017, 'Region-based Mixture Models for human action recognition in low-resolution videos', Neurocomputing, vol. 247, pp. 1-15.
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FOR Codes: Image Processing, Electronic Information Storage and Retrieval Services