Project Abstract / Summary: The 60% of accidents in railways are due to track problems which lead to derailment. This project presents the system for automatic crack detection in railway tracks. This project can help to detect the cracks or breakages in railway tracks if any before the train passes and the alert signal is sent to the train operator and the power line is tripped off immediately.

The automatic crack detection system is constructed using vibration sensors and MEMS technology. Vibration sensor is used to detect the crack which has speed response to vibrations. MEMS are used to detect the track deviations and dislocations. MEMS is Micro Electro Mechanical System that combines electrical and mechanical components.In this project MEMS are used to check the status of the railway tracks and RF modules are used to transmit and receive the signal.

PIC micro controller is used to compare the input signals with threshold value at transmitter side. MMIC (Monolithic Microwave Integrated Circuit) is used for continuous monitoring of MEMS.

This project is cost effective and simple. Why did you choose to work on this project topic: The 60% of accidents in railways are due to track problems which lead to derailment.Derailment problem often result in loss of human lives and damage to property. To prevent such kind of accidents we chose to work on this project topic. Project Category: Electrical / Electronics / Communication ------------------------------------------------------ Institute/College Name: Easwari Engineering College City: Chennai State: Tamilnadu Participating Team From: Final Year.

This project presents a cost effective. The main aim of project is to design and develop an automatic rail crack detection system based on infrared technology.

Here we propose an innovative approach to detect railway track crack as this system detects crack based on image processing. Many image preprocessing steps is used to detect railway track crack. As image is prone to noise. Pustoj blank cennika. System converts image to grayscale image and uses filtering to remove noise from image. Noise removal helps to detect crack more accurately. Image luminous level is increased and image is converted to binary image.

This helps system to detect only crack and helps to remove other unwanted objects. Image once converted to binary image, holes are filled by using image processing method this helps to reject all smaller objects which are not required for crack detection. Intensity value is used for accuracy purpose. Blob analysis method is used to detect large blobs. System detects crack based on number of connected components. System detects crack based on number of blobs involved and mentions whether crack exist or not. Using bounding box functionality, system displays rectangular box around the blob.

This system used during railway track inspection. The proposed system is able to detect deeper cracks with 80% success rate and minor cracks with 50-60% accuracy.