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A semi-automated battery-operated (Figure 3.2) cart is designed with ultrasonic sensors attached to the front. This ultrasonic sensor sends high-frequency sound waves through the transmitter head, which then goes and reflects back on hitting the surface of the track, and comes back to the receiver head. This is then converted to distance.

A threshold distance is set, by measuring the distance between the sensor and the surface of the track. At the time of inspection, if the distance exceeds or is less than the threshold distance, it indicates that a crack is present on the track. When a crack as such is detected, the cart stops there. This is the first level of testing.

As a second level of testing, once the cart stops, a camera attached to the front of the cart is triggered on. This camera then takes a picture of the track where the crack is present. This picture is then sent to the Amazon S3 bucket and image processing is performed on the captured image. This is done so as to detect if it is a major or a minor crack.

In case of a major crack, the cart will store the GPS location and return to the previous checkpoint. In case of a minor crack, the cart will continue with its inspection. In either case, the GPS location and the details of the crack and the status are stored in Amazon EC2 and are sent immediately to the concerned authorities to be rectified as soon as possible.

Figure 3.2 Flow diagram of the automated system.

Cart development and sensor:

For the development of the cart, 12V DC motors can be used. Motor controller L298N is used to control the movement of the cart. Ultrasonic sensor HC-SR04 sends high-frequency sound waves and calculates the distance of the reflected wave. This sensor detects the minute defects with high accuracy. This is a non-destructive testing method.

Raspberry Pi 3:

RPi is a low-cost, small credit-card-sized computer. RPi3 is faster than Arduino.

Cloud server and Image processing:

Host server is AWS EC2. Date, time, message status, ID, sensor value, latitude and longitude are stored in the EC2 database. A picture of the defect is taken and stored in AWS S3 bucket. Gaussian Mixture Model can be used for image processing which comes under supervised learning.