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CAT in CONTROL of PROGRAMMING Mocha, helping me work on a new program. About me and this website I am Bob Freeth, a retired radio ham G4HFQ and in my late 70s. Over the past 20 years I have written numerous programs that are of interest to radio hams. Most of the programs are for managing the memories and settings of Yaesu radios. The Order page is where you will find the most recent news on what is happening to this website. The Download page is where you will find links for downloading. Product support is provided by me only for people who have purchased licenses in the past. Email me at the address bob.freeth@g4hfq.co.uk. There is also a FAQ page on my website here. All support emails are deleted once the issue is resolved. The website g4hfq.co.uk does not collect any personal data, has no databases nor any tracking cookies or web analytics. If you visit any of the links to external websites the situation is beyond my control and may well be different to this site. |
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Automotive Crack Detection for Railway Track Using Ultrasonic Sensorz. Crack Detection System For Railway Track By Using Ultrasonic And Pir Sensor' IJAICT. This project proposes a novel.
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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.
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As image is prone to noise. 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.
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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.
Official URL:AbstractThis report present the development of Design a Cost Effective Railway Crack Detection System Using Ultrasonic Sensors and Arduino UNO. Railway crack continues to occur in modern architecture due to contact stresses between the wheel of the train and the surface of the railway. Thus, it will bought huge losses to the people’s lives and property.
In order to reduce railway crack in railway track, a railway crack detector device installed into an essential. The approach of railway crack detection system is by using two ultrasonic sensors to detect crack for that occurs in railway track such as in KTM komunter train area.
The ultrasonic sensors then integrated with Arduino itself and display message to the maintenance team so that the maintenance could done faster. Besides, the maintenance team can interact with the crack detector system and the system will continue to scan for any defect at the railway tracks by Graphical User Interface (GUI) program whether is in safety run mode or normal run mode. Arduino UNO is adopted as main control chip, data logging are achieved by using Serial Monitor and LCD functions as display message. The result of test shows this design has characters of cost effective, and will be widely used.
Communication with the users is fulfilled through the GUI in this crack detection system. Its consists of safety run mode and normal run mode, which the device will run at its speed according to the mode. Item Type:Monograph(Project Report)Uncontrolled Keywords:Ultrasonics, Programmable controllers, Ultrasonics imaging - Data processing, Ultrasonics, equipment - Design and constructionSubjects:Divisions:Depositing User:Date Deposited:20 Sep 2017 09:04Last Modified:20 Sep 2017 09:04URI:Actions (login required)View Item.