| dc.description.abstract | Railway transportation plays an important role
in global transportation systems, providing efficient and
rapid movement of passengers and goods. The safety and
reliability of train travel heavily depend on the railway
tracks' quality. rail surface defects present a substantial risk, which can result in accidents and service interruptions.
Identifying rail surface defects presents several challenges,
particularly in extracting discriminant features for effective
defect detection. This task is complex and non- trivial due
to the diverse nature of defects and their appearances.
Timely identification is crucial to ensure railway
operations' safety and continuous functioning. This paper
proposes performing image classification using
Convolutional Neural Networks (CNNs) to detect defects on
rail surfaces. A publicly available dataset of 1838 images is
split 70-20-10 for training, testing, and validation. Rail
surface images are preprocessed through resizing, noise
reduction, and pixel normalization for CNN compatibility.
Data augmentation, including rotation, zooming,
brightness adjustment, channel shifting, and horizontal
flipping, generates diverse samples with varied
perspectives. Convolutional Neural Networks are
employed, utilizing transfer learning techniques such as
Mobilenet-V2, VGG-16, SqueezeNet, and Inception-V3 to
train the classification model with the addition of channel
attention. Several pre-trained models are evaluated, and the
fine-tuned Inception-V3 model demonstrates a
classification accuracy of 95.76%. This research
contributes to the development of the railway industry,
offering cost-effective solutions for detecting defects in
railway tracks in earlier stages | en_US |
