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.

Recently, various dynamic risk analysis methods have been suggested for estimating the risk index by predicting the possibility of accidents and damage in real time. It is necessary to quickly estimate the risk of an accident by predicting the probability and consequences of accidents, which are quantitative criteria for ship risk assessment. This study aimed to develop an algorithm for predicting the consequences of accidents in real time to perform a dynamic risk assessment and, using this algorithm, formulate a ship accident–response procedure that can be used in fire accidents during LNG bunkering. The risk of fire was estimated by predicting the hole size due to changes in the liquefied natural gas (LNG) transport conditions and the consequence based on the fire accidents scenario in LNG-fueled ships. The prediction model was trained with the hole prediction data using Ansys CFX and with the consequence data using DNV Phast, and the consequences of fire were compared and evaluated by applying the trained results. A method for estimating the size of the fire based on the predicted consequence is proposed, which supports fast decision making in fire accidents during LNG bunkering by identifying the potential size of the fire at the beginning of an accident.