Classification of Hull Blocks of Ships Using CNN with Multi-View Image Set from 3D CAD Data

• Published in: Journal of marine science and engineering 2023-02, Vol.11 (2), p.333
• Main Authors: Chon, Haemyung, Oh, Daekyun, Noh, Jackyou
• Format: Article
• Language: English
• Subjects: 3D CAD data; Accuracy; Artificial neural networks; binarization; CAD-CAM systems; Classification; Computer programs; convolutional neural network; Data acquisition; Datasets; Design; Identification; Image acquisition; Image classification; Locations (working); multi-view imageset; Neural networks; Predictions; Scheduling; ship hull block; Ship hulls; Shipbuilding; Shipbuilding industry; Ships; Shipyards; Three dimensional printing; Tracking systems; Training; Workers; Workshops
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11020333

In order to proceed with shipbuilding scheduling involving hundreds of hull blocks of ships, it is important to mark the locations of the hull blocks with the correct block identification number. Incorrect information about the locations and the identification numbers of hull blocks causes disruption in the shipbuilding scheduling process of the shipyard. Therefore, it is necessary to have a system for tracking the locations and identification numbers of hull blocks in order to avoid time loss due to incorrectly identified blocks. This paper proposes a method to mark the identification numbers, which are necessary for the tracking system of hull blocks. In order to do this, 3 CNN (convolutional neural network) models, VGG-19, Resnet-152V2, and Densenet-201, are used to classify the hull blocks. A set of multi-view images acquired from 3D CAD data are used as training data to obtain a trained CNN model, and images from 3D printer-printed hull block models are used for the test of the trained CNN model. The datasets used for training and prediction are Non-Thr and Thr datasets, that each included both binarized and non-binarized datasets. As a result of end-to-end classification experiments with Non-Thr datasets, the highest prediction accuracy was 0.68 with Densenet-201. A total of 4050 experimental conditions were constructed by combining the threadhold of the Thr training and testing dataset. As a result of experiments with Thr datasets, the highest prediction accuracy of 0.96 was acquired with Resnet-152V2, which was trained with a threshold of 72 and predicted with a threshold of 50. In conclusion, the classification of ship hull blocks using a CNN model with binarized datasets of 3D CAD data is more effective than that using a CNN model with non-binarized datasets.