The objective of this project is to develop an automated system using Raspberry Pi and image processing to detect and respond to cracks in railway tracks, improving inspection efficiency and safety.
This
project presents a system for railway track detection utilizing a Raspberry Pi
in conjunction with a USB webcam and DC motors to build a robotic surveillance
platform. The system leverages advanced image processing techniques, including
Canny edge detection, dilation, and histogram analysis in grayscale, to
continuously monitor and assess the condition of railway tracks. The Raspberry
Pi processes the live video feed from the USB webcam to detect any potential
cracks or defects in the track. When no cracks are detected, the robot proceeds
forward along the track. Conversely, if a crack is identified, the robot halts
to prevent further damage and facilitate timely maintenance. This automated
approach enhances track inspection efficiency and contributes to improved
railway safety.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Hardware components:
Software components: