Analysis and Test of Concrete Surface Crack of Railway Bridge Based On Deep Learning

Gong, Xingqi; Li, Quan; Mei-ling, Zhou; Jiang, Huifeng

doi:10.1109/itoec49072.2020.9141789

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Lastly, reference [151] mentioned the Singapore Mass Rapid Transit (SMRT) dataset, however, also in this case, the dataset seems not to be accessible. Bridges [163] The * mark indicates data not related to the railway domain.…”

Section: E Datasetsmentioning

confidence: 99%

“…In [163], the authors used a YOLOv3 network, pre-trained on the VOC2007 dataset [131], to detect cracks on bridges' beams. Since YOLO is not so good with small-scale objects, images acquired by a CCD camera were first cropped in 64 smaller images.…”

Section: B Bridgementioning

confidence: 99%

“…Concerning datasets, as for the previous areas, the authors used some existing datasets to build transfer learning approaches: reference [163] used the VOC2007 dataset [131] to pre-train the proposed architecture, while in reference [36], the model has been trained on the [94] dataset, although in our understanding this is related to generic concrete structures cracks. On the other hand, [158] built a dataset from scratch related to defects on tunnels' lining from a proprietary acquisition system.…”

Section: Datasetsmentioning

confidence: 99%

“…Differently, for other subcomponents, the outcome was not expected, especially for cracks in tunnels and bridges (which account for only 8 papers out of 95), since such defects have already been analysed in other domains. Evidence for this are: i) a study related to the road sector [167]; ii) the datasets it uses (CRACK500 [168], [169], GAPs384 [170], and CFD [171]), which are related to different types of cracks; iii) two approaches based on transfer learning we reviewed in this SLR, i.e., reference [36], using a general crack dataset [94], and reference [163], using the VOC2007 dataset [131]. "Rolling Stock" show a growing trend (with the latter mostly pushed by papers on Bogies, suggesting this to be a hot topic in the future), while those focusing on "Tunnel & Bridge" appears almost steady.…”

Section: A Response To Rq1mentioning

confidence: 99%

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A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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Section: E Datasetsmentioning

confidence: 99%

Section: B Bridgementioning

confidence: 99%