CRACKWATCH AI: REAL-TIME RAIL SURFACE CRACK DETECTION USING DEEP CONVOLUTIONAL ARCHITECTURES

Abstract

Railway transportation is one of the most important
modes of transportation for passengers and freight
movement across the world. The safety and
reliability of railway infrastructure are highly
dependent on the condition of railway tracks.
However, railway tracks are continuously exposed
to heavy mechanical loads, vibration,
environmental stress, temperature variations, and
material fatigue, which may lead to structural
defects such as cracks, fractures, corrosion, surface
wear, and damaged fasteners. If these defects are
not detected at an early stage, they may result in
severe railway accidents, derailments,
infrastructure damage, economic losses, and threats
to human life. Traditional railway inspection
methods mainly rely on manual inspection and
periodic monitoring vehicles, which are timeconsuming,
labor-intensive, costly, and prone to
human error. To overcome these limitations, this
project proposes an intelligent railway fault
detection system using deep learning and computer
vision techniques. The proposed system utilizes
convolutional neural networks and advanced object
detection models to automatically analyze railway
track images and classify them into defective and
non-defective categories. Image preprocessing and
data augmentation techniques such as rotation,
flipping, scaling, and brightness adjustment are
applied to improve model accuracy and
generalization capability under different
environmental conditions. The system supports
real-time fault detection and can be deployed on
lightweight edge devices for continuous monitoring
applications. Experimental analysis demonstrates
that the proposed model achieves high detection
accuracy with low computational complexity,
making it suitable for practical railway monitoring
systems. The developed system improves railway
safety, reduces dependency on manual inspections,
minimizes maintenance costs, and supports
predictive maintenance strategies for modern smart
railway infrastructure.