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• W3116425700 abstract "In this paper, we investigate a solution to the problem of underwater pipeline segmentation, based on an unbalanced dataset generated by a deterministic algorithm which employs computer vision techniques. We use manually selected masks to train two types of neural networks, U-Net and Deeplabv3+, to solve the same semantic segmentation task. We show that neural networks are able to learn from imperfect datasets, artificially generated by other algorithms. Deep convolutional architectures outperform the algorithm based on computer vision techniques. In order to find the best model, a comparison was made between the two architectures, thereby concluding that Deeplabv3+ achieves better results and features robust operation under adverse environmental conditions." @default.
• W3116425700 created "2021-01-05" @default.
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• W3116425700 date "2021-01-24" @default.
• W3116425700 modified "2023-09-23" @default.
• W3116425700 title "Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets" @default.
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• W3116425700 doi "https://doi.org/10.23919/eusipco47968.2020.9287605" @default.
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