Automatic Condition Monitoring of Railway Overhead Lines from Close-Range Aerial Images and Video Data

Andert, Franz; Kornfeld, Nils; Nikodem, Florian; Li, Haiyan; Kluckner, Stefan; Gruber, Laura; Kaiser, Christian

doi:10.1109/icuas48674.2020.9213972

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Tables 3 and 4, whose structure and aim are described in Section III-E, summarise the papers we found within this Railway Area. Catenary Support Device [95], [96], [97] [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110] [111], [112], [113], [114], [115], [116] Pantograph & Arcs [117], [118] [101], [119], [120], [121] [122], [123], [124] Catenary Wires [125], [126], [127], [128], [109] [129]…”

Section: Pantograph and Catenarymentioning

confidence: 99%

“…Then, VGG16 and ResNet-101 have been tested as feature extractors, with the former resulting slightly better both in terms of mAP and AP, even if a little bit slower than the latter. Finally, in [115], the authors proposed to detect rod insulators within images captured through two drones. Analyses were conducted considering two types of drones, custom and off-the-shelf, evaluating different pre-processing strategies to obtain optimal images.…”

Section: A Catenary Support Device (Csd)mentioning

confidence: 99%

“…Interestingly, while in some cases other DL or image processing approaches have been implemented downstream of these identifiers to detect anomalies, in other cases the presented approach is limited to target detection. For instance, [115] focuses on insulators detection, [111] focuses on brace sleeve screws identification, while [127] and [129] propose architectures for dropper detection. It is worth noting that "simple" object detection applications can also directly involve defect detection, such as the identification of Arcs (i.e., an object), which indicates possible deterioration for pantographs or contact wires (e.g., [119], [120]).…”

Section: Findings On Pantograph and Catenarymentioning

confidence: 99%

“…The small-scale object detection introduces difficulties in tasks such as defect inspection of CSD and bogies components, leading some researchers to focus only on component detection (e.g., [111], [115], [129]). Building multi-stage approaches as discussed in the response to RQ2 could be a suitable solution to deal with this issue.…”

Section: ) Small-scale Object Detectionmentioning

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: Pantograph and Catenarymentioning

confidence: 99%

Section: A Catenary Support Device (Csd)mentioning

confidence: 99%

Section: Findings On Pantograph and Catenarymentioning

confidence: 99%

Section: ) Small-scale Object Detectionmentioning

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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Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research

Chen,

Frøseth,

Derosa

et al. 2024

Sensors

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In this paper, a different approach to the traditional literature review—literature systematic mapping—is adopted to summarize the progress in the recent research on railway catenary system condition monitoring in terms of aspects such as sensor categories, monitoring targets, and so forth. Importantly, the deep interconnections among these aspects are also investigated through systematic mapping. In addition, the authorship and publication trends are also examined. Compared to a traditional literature review, the literature mapping approach focuses less on the technical details of the research but reflects the research trends, and focuses in a specific field by visualizing them with the help of different plots and figures, which makes it more visually direct and comprehensible than the traditional literature review approach.

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Automatic Condition Monitoring of Railway Overhead Lines from Close-Range Aerial Images and Video Data

Cited by 2 publications

References 13 publications

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

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

Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research

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