We propose an automatic welding defect detection system, which is a computer software application, using Radiation Sourced images .
Our software can take the image of a pipe with its welding defects and compare them to an optimal database where the “perfect” constitution of the image is stored, and indicate deficiencies in real-time, using Machine Learning Algorithms (ML).
Our proposed method for automatic detection and classification of faults and defects in the radiographic images of welded joints is attained through an exposure technique of double wall double image (DWDI). There has been a limited study done in this technique so far. Initially, the Radiography image using DWDI  is loaded from a source. The image is compared to the Machine Learnt Database through our software application to detect variation from the norm. Besides, it denoise images and, enhance image quality. It is an automatic real-time detection and classification software to assist inspectors in identifying defects in welded joints efficiently.
We Provide the creative and cost-effective solution in nondescriptive testing through computer-aided technology to create a fully-integrated construction and maintenance solution to the North American Energy Market by way of a collaborative approach with our partners and clients

Luminous Canada Inc.

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