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Title |
Development of Fender Segmentation System for Port Structures using Vision Sensor and Deep Learning
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Authors |
민지영(Jiyoung Min) ; 유병준(Byeongjun Yu) ; 김종혁(Jonghyeok Kim) ; 전해민(Haemin Jeon) |
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DOI |
https://doi.org/10.11112/jksmi.2022.26.2.28 |
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Keywords |
비전센서; 딥러닝; 방충설비 세분화; 항만시설물 Vision sensor; Deep learning; Fender segmentation; Port structure |
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Abstract |
As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.
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