Railway transportation requires constant inspections and immediate maintenance to ensure public safety. Traditional manual inspections are not only time consuming, and expensive, but the accuracy of defect detection is also subjected to human expertise and efficiency at the time of inspection. Computing and Robotics offer automated IoT based solutions where robots could be deployed on rail-tracks and hard to reach areas, and controlled from control rooms to provide faster inspection. In this paper, a novel automated system based on robotics and visual inspection is proposed. The system provides local image processing while inspecting, cloud storage of information that consist of images of the defected railway tracks only, and robot localization within a range of 3-6 inches. The proposed system utilizes state of the art Machine Learning system and applies it on the images obtained from the tracks in order to classify them as normal or suspicious. Such locations are then marked and more careful inspection can be performed by a dedicated operator with very few locations to inspect (as opposed to the full track). The project aims in designing robust railway crack detection scheme (RRCDS) using Machine learning which avoids the train accidents. Raspberry pi and pi camera-based railway track detection system. And also, this system capable of GPS module for location tracking. If the robot detect abnormality on tracks, it can capture that image and sending this image into the cloud.   The GPS is the acronym for Global positioning system. This GPS receiver is capable of identifying the location in which it was present in the form of latitude and longitudes. This information is very useful and can be processed for alerting the boat drivers. The GPS gives the data received from the satellites. For this information the GPS communicates with at least three satellites in the space.   In this Project presents an automotive localization system based on robot using GPS and cloud services. The system permits localization of the automobile and transmitting the position to the authorities on their cloud service. The project consists of an automatic Robust robot system which is interfaced with DC motors and relay as DC motor driver. This project consists of raspberry pi 3 and pi camera for capturing and sensing image to the respective authorities. This project contains machine learning CNN image processing technology is used to images obtained from the tracks in order to classify them as normal or suspicious.SD card is providing the initial storage of operating system to the raspberry pi. To achieve this task raspberry pi loaded program written in embedded C language.    

The main objectives of the project are:  

• Automated railway TRACK CRACK detection using machine learning CNN algorithm.
• GPS based location tracking system.
• IoT based cloud storage system.

    The major building blocks of this project are:  

• Rechargeable Battery.
• Raspberry Pi3 Processor.
• SD Card.
• Pi camera.
• GPS Module.
• LCD Display.
• Buzzer.
• LED indicators.
• DC motors with L293d moor driver.
• Robot chassis.

  Software’s used:  

1. Raspbian OS.
2. Python Language.
3. Machine learning image processing (CNN) algorithm.

      Block Diagram: video: