A Novel Toolbox for Induced Voltage Prediction on Rail Tracks Due to AC Electromagnetic Interference Between Railway and Nearby Power Lines

Shabbir, Md Nasmus Sakib Khan; Wang, Chenyang; Liang, Xiaodong; Adajar, Emerson

doi:10.1109/tia.2023.3234935

Cited by 2 publications

(1 citation statement)

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“…Railroad power transmission lines play a vital role in modern rail transportation systems, providing a stable and reliable channel for the transmission of power to trains 1 , 2 . However, when power transmission lines come into contact with conductive materials such as metal foils or metal kite wires, short circuits may be triggered, resulting in loss of train power.…”

Section: Background and Summarymentioning

confidence: 99%

RailFOD23: A dataset for foreign object detection on railroad transmission lines

Chen,

Yang,

Feng

et al. 2024

Sci Data

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Artificial intelligence models play a crucial role in monitoring and maintaining railroad infrastructure by analyzing image data of foreign objects on power transmission lines. However, the availability of publicly accessible datasets for railroad foreign objects is limited, and the rarity of anomalies in railroad image data, combined with restricted data sharing, poses challenges for training effective foreign object detection models. In this paper, the aim is to present a new dataset of foreign objects on railroad transmission lines, and evaluating the overall performance of mainstream detection models in this context. Taking a unique approach and leveraging large-scale models such as ChatGPT (Chat Generative Pre-trained Transformer) and text-to-image generation models, we synthesize a series of foreign object data. The dataset includes 14,615 images with 40,541 annotated objects, covering four common foreign objects on railroad power transmission lines. Through empirical research on this dataset, we validate the performance of various baseline models in foreign object detection, providing valuable insights for the monitoring and maintenance of railroad facilities.

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Section: Background and Summarymentioning

confidence: 99%