Automatic rail fault track detection for Indian railways

Parvathy, A. K.; Mathew, Mebin George; Justus, Saji; Ajan, A.

doi:10.1109/cesys.2017.8321251

Cited by 10 publications

(1 citation statement)

References 4 publications

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“…The cress cross tracks are almost present in every part of India. Keeping in mind the security of people also the free running railway without any problem, we have to focus in the safety part of this system [1]. In India, the railway network accounts for over 80% of all transportation.…”

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confidence: 99%

Fault Diagnosis in Railway Track using Efficient Net based CNN

Mordia¹,

Verma²

2022

YMER

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Railway transportation is the most cost-effective and convenient means of passenger travel in India. The cress cross tracks are almost present in every part of India. Keeping in mind the security of people also the free running railway without any problem, we have to focus in the safety part of this system [1]. In India, the railway network accounts for over 80% of all transportation. Approximately 60% of accidents occur at crossings of railway tracks because of a railway track fracture, resulting in the loss of valuable life and economic damage. As a result, new technology is required for both fault detection and object detection in railway tracks. This technology must be resilient, efficient, and steady [2]. This paper presents a vision based method to find some common defects in railways. Some images have been collected of railway track and image processing method is used to preprocess these images and to detect the features related to defective parts. An EfficientNet based CNN model is developed to detect the defects which uses global average pooling, adam as optimizer, softmax as activation function and categorical_crossentropy as loss function. This research result consists of a classification report as defective and non-defective parts or image with accuracy of 91 percentages over 30 epochs. Keywords: Rail Track Images, Defects, Defect Detection Method, CNN and Defect Classification.

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confidence: 99%