Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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Comparison Study of the Performance of CNN Models with Multi-view Image Set on the Classification of Ship Hull Blocks

다시점 영상 집합을 활용한 선체 블록 분류를 위한 CNN 모델 성능 비교 연구

  • Chon, Haemyung (Department of Naval Architecture and Ocean Engineering, Kunsan National University) ;
  • Noh, Jackyou (Department of Naval Architecture and Ocean Engineering, Kunsan National University)
  • 전해명 (군산대학교 조선해양공학과) ;
  • 노재규 (군산대학교 조선해양공학과)
  • Received : 2020.03.02
  • Accepted : 2020.03.24
  • Published : 2020.06.20
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Abstract

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

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References

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