Automatic Detection of Welding Defects Using Faster R-CNN

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.