セマンティックセグメ​ンテーションを使った​転移学習と、その学習​データをもとにテスト​するにはどうすればよ​いですか？

コンクリートののひび割れ画像をセマンティックセグメンテーションを用い、学習し、テストさせ、ひび割れがある個所、ない箇所を色分けするスクリプトが知りたい。

Answer 1

[Naga]

Hi 大暉 高井,

I understand you want to use semantic segmentation to color-code areas with cracks and areas without cracks in concrete images, please follow the below steps to achieve that:

1. MATLAB provides pre-trained models for semantic segmentation, such as DeepLabv3+ or SegNet, load one of these pre-trained models using the deeplabv3plusLayers or segnetLayers functions.
2. Depending on the model you choose replace the last layers of the pre-trained model with new layers for your specific task.
3. Modify the number of output classes to match your requirement (e.g., 2 classes for crack and non-crack).
4. Use the trainNetwork function to train the customized model using the training dataset.
5. Fine tune the parameters such as the number of epochs, learning rate, and mini-batch size.
6. Use the trained model to predict the segmentation masks for the testing dataset.

For more understanding about semantic segmentation please refer to the following documentations:

1. Semantic segmentation: https://www.mathworks.com/help/vision/semantic-segmentation.html?s_tid=CRUX_lftnav
2. Examples: https://www.mathworks.com/help/vision/examples.html?category=semantic-segmentation&s_tid=CRUX_topnav

Hope this helps!