Abstract
In the shipbuilding industry, the non-destructive testing for welding quality inspection is mainly used for the permanent storage of the testing results and the radio-graphic testing which can visually inspect the interior of the welded part. Experts are required to properly detect the test results and it takes a lot of time and cost to manually Interpret the radio-graphic testing image of the structure over 500 blocks. The algorithms that automatically interpret the existing radio-graphic testing images to extract features through image pre-processing and classify the defects using neural networks, and only partial automation is performed. In order to implement the feature extraction and classification in one algorithm and to implement the overall automation, this paper proposes a method of automatically detecting welding defect using Faster R-CNN which is a deep learning basis. We analyzed the data to learn algorithms and compared the performance improvements using data augmentation method to artificially increase the limited data. In order to appropriately extract the features of the radio-graphic testing image, two internal feature extractors of Faster R-CNN were selected, compared, and performance evaluation was performed.
Highlights
The welding process accounts for more than 60% of the entire process in the shipbuilding and offshore sector [1]
We propose an algorithm that automatically detects the welding defects in radiographic images by employing Faster R-convolutional neural network (CNN) that shows high-performance in terms of accuracy
As the size of defects belonged to an object that was smaller than the size of existing objects, we set the size of the anchor box and aspect ratio to be suitable for small objects, and set the number of region proposal recommendations through an experiment
Summary
The welding process accounts for more than 60% of the entire process in the shipbuilding and offshore sector [1]. There are various technologies such as radiographic testing (RT), ultrasonic testing (UT) and magnetic testing (MT) used as non-destructive testing (NDT). Ship owners prefer RT whose results can be stored permanently and that can visually check the inside of the weld of all materials to other types of NDT. Technicians directly perform welding testing on structures of 500 blocks or more to inspect the welding process in domestic and overseas shipyards. Since welding information of more than 2000 locations per block is manually prepared, omissions and errors commonly occur, which requires additional work, resulting in a huge amount of time and cost. To derive a consistent and rational result from testing that is manually conducted, there is a need for an automation and objectification system of testing that improves inspectors’ understanding
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