Linnosmaa, Joonas

Pakarinen, Janne

Pohja, Rami

Zeb, Akhtar

English

Semantic segmentation is the task of assigning a class or label to every pixel of the image. In addition to detecting objects, the semantic segmentation models also predict the shape, size, and location of each object in images. Deep learning-based segmentation has been used in challenging object detection tasks in several domains, such as autonomous vehicles, satellite images, and medical image diagnostics.Within materials science, timely and reliable detection of creep voids in solid materials operating under high temperatures is vital for better life cycle management of valuable components. In this study, we present the application of a semantic image segmentation model for detecting creep voids in SEM images.The semantic segmentation models generally consist of an encoder network followed by a decoder network. The encoder is usually a pre-trained classification network, such as VGG or ResNet. The decoder network projects the discriminative features learned by the encoder onto the pixel space, performing the classification task. To distinguish the creep voids from the normal surface of copper samples, we applied the DeepLab-v3+ model with encoders pre-trained on large datasets. Training the model with only 250 images for 200 epochs, we obtained a mean IoU score of 0.994 and a dice loss of 0.003. The generated segmentation maps provide information about the area fraction and number of creep voids

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This document is downloaded from theVTT’s Research Information Portalhttps://cris.vtt.fiVTThttp://www.vtt.fiP.O. box 1000FI-02044 VTTFinlandBy using VTT’s Research Information Portal you are bound by thefollowing Terms & Conditions.I have read and I understand the following statement:This document is protected by copyright and other intellectualproperty rights, and duplication or sale of all or part of any of thisdocument is not permitted, except duplication for research use oreducational purposes in electronic or print form. You must obtainpermission for any other use. Electronic or print copies may not beoffered for sale.VTT Technical Research Centre of FinlandSemantic segmentation for the analysis of creep voids in metallicmaterialsZeb, Akhtar; Linnosmaa, Joonas; Pohja, Rami; Pakarinen, JannePublished: 16/11/2022Link to publicationPlease cite the original version:Zeb, A., Linnosmaa, J., Pohja, R., & Pakarinen, J. (2022). Semantic segmentation for the analysis of creep voidsin metallic materials. Poster session presented at FCAI AI Day 2022, Espoo, Finland.Download date: 25. Jul. 2023Introduction• High-temperature metallic materials suffer from creep dueto mechanical stresses• Prolonged creep condition causes material deformationand component failure (Fig. 1)• Formation of creep voids in material structure is a primeindication of creep phenomenon• Timely & accurate detection of creep voids helps in betterlife cycle management of valuable products• Semantic image segmentation reduces human errors andspeeds up the analysis processSemantic image segmentation• Assigning a class or label to every pixel of the image• Information about the location, size, and shape of objects• Several applications including medical imaging, objectdetection, and recognition tasks• Segmentation models generally consist of an encodernetwork followed by a decoder network• Encoder is usually a pre-trained classification network,such as VGG or ResNet• Decoder projects the discriminative features learned bythe encoder into the pixel space, performing classificationModel performance• Training time about 17 minutes (wall time) for 200 epochs• Testing IoU score of 0.994 and dice loss of 0.003• Good agreement between model prediction and groundtruth (Fig. 2)• Information about area fraction (Fig. 3) and number ofcreep voids in an image (Fig. 4).Case study – creep voids in copper samples• SEM images of oxygen-free phosphorous-doped coppersample surfaces• Our task is to distinguish creep voids (white pixels) fromthe normal surface (black pixels) (Fig. 2)• DeepLab-v3+ [2] model built on top of CNN architecturewith ResNet encoder pre-trained on ImageNet dataset• Model training 251 (70%), validation 54 (15%), andtesting 55 (15%) images• PyTorch-based Segmentation Models [1] library and theGoogle Colab environment for model implementationConclusion• Timely and reliable detection of creep voids is vitalfor better life cycle management of valuable assets• Knowledge of publicly available pre-trained encoderscan be utilized to build new models with few images• Semantic segmentation model accurately segmentscreep voids in SEM images• Information about the density and area fraction ofcreep voids is obtained within a few seconds• Further work on segmentation of various types ofcreep voids and generalization of the trained modelwww.vttresearch.comAkhtar Zeb, Joonas Linnosmaa, Rami Pohja, Janne PakarinenVTT Technical Research Centre of Finland LtdContact: Akhtar Zeb, Research ScientistTel. +358 40 681 9332, akhtar.zeb@vtt.fiSemantic segmentation for the analysisof creep voids in metallic materials[1] Yakubovskiy, P. 2022. Segmentation Models. Online: https://smp.readthedocs.io/en/latest/index.html[2] Chen, L.C., Zhu, Y. 2018. Semantic Image Segmentation with DeepLab in TensorFlow. Google AIBlog. Available at: https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.htmlFig. 1. Creep voids in materials and different stages of creep behavior00.511.522.50 5 10 15 20 25 30 35Area fraction (%)Image numberground truthpredictedFig. 3. Area fraction of creep voids, ground truth vs. predicted1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 121 1 12 2 2 2 23 340 012 2 240123450 5 10 15 20 25 30 35 40Number of creep voids inan imageImage numberground truthpredictedFig. 4. Number of creep voids in an image, ground truth vs. predictedFig. 2. Comparison between ground truth and predicted masks

Semantic segmentation for the analysis of creep voids in metallic materials

This document is downloaded from theVTT’s Research Information Portalhttps://cris.vtt.fiVTThttp://www.vtt.fiP.O. box 1000FI-02044 VTTFinlandBy using VTT’s Research Information Portal you are bound by thefollowing Terms & Conditions.I have read and I understand the following statement:This document is protected by copyright and other intellectualproperty rights, and duplication or sale of all or part of any of thisdocument is not permitted, except duplication for research use oreducational purposes in electronic or print form. You must obtainpermission for any other use. Electronic or print copies may not beoffered for sale.VTT Technical Research Centre of FinlandSemantic segmentation for the analysis of creep voids in metallicmaterialsZeb, Akhtar; Linnosmaa, Joonas; Pohja, Rami; Pakarinen, JannePublished: 16/11/2022Link to publicationPlease cite the original version:Zeb, A., Linnosmaa, J., Pohja, R., & Pakarinen, J. (2022). Semantic segmentation for the analysis of creep voidsin metallic materials. Poster session presented at FCAI AI Day 2022, Espoo, Finland.Download date: 20. Feb. 2023Introduction• High-temperature metallic materials suffer from creep dueto mechanical stresses• Prolonged creep condition causes material deformationand component failure (Fig. 1)• Formation of creep voids in material structure is a primeindication of creep phenomenon• Timely & accurate detection of creep voids helps in betterlife cycle management of valuable products• Semantic image segmentation reduces human errors andspeeds up the analysis processSemantic image segmentation• Assigning a class or label to every pixel of the image• Information about the location, size, and shape of objects• Several applications including medical imaging, objectdetection, and recognition tasks• Segmentation models generally consist of an encodernetwork followed by a decoder network• Encoder is usually a pre-trained classification network,such as VGG or ResNet• Decoder projects the discriminative features learned bythe encoder into the pixel space, performing classificationModel performance• Training time about 17 minutes (wall time) for 200 epochs• Testing IoU score of 0.994 and dice loss of 0.003• Good agreement between model prediction and groundtruth (Fig. 2)• Information about area fraction (Fig. 3) and number ofcreep voids in an image (Fig. 4).Case study – creep voids in copper samples• SEM images of oxygen-free phosphorous-doped coppersample surfaces• Our task is to distinguish creep voids (white pixels) fromthe normal surface (black pixels) (Fig. 2)• DeepLab-v3+ [2] model built on top of CNN architecturewith ResNet encoder pre-trained on ImageNet dataset• Model training 251 (70%), validation 54 (15%), andtesting 55 (15%) images• PyTorch-based Segmentation Models [1] library and theGoogle Colab environment for model implementationConclusion• Timely and reliable detection of creep voids is vitalfor better life cycle management of valuable assets• Knowledge of publicly available pre-trained encoderscan be utilized to build new models with few images• Semantic segmentation model accurately segmentscreep voids in SEM images• Information about the density and area fraction ofcreep voids is obtained within a few seconds• Further work on segmentation of various types ofcreep voids and generalization of the trained modelwww.vttresearch.comAkhtar Zeb, Joonas Linnosmaa, Rami Pohja, Janne PakarinenVTT Technical Research Centre of Finland LtdContact: Akhtar Zeb, Research ScientistTel. +358 40 681 9332, akhtar.zeb@vtt.fiSemantic segmentation for the analysisof creep voids in metallic materials[1] Yakubovskiy, P. 2022. Segmentation Models. Online: https://smp.readthedocs.io/en/latest/index.html[2] Chen, L.C., Zhu, Y. 2018. Semantic Image Segmentation with DeepLab in TensorFlow. Google AIBlog. Available at: https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.htmlFig. 1. Creep voids in materials and different stages of creep behavior00.511.522.50 5 10 15 20 25 30 35Area fraction (%)Image numberground truthpredictedFig. 3. Area fraction of creep voids, ground truth vs. predicted1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 121 1 12 2 2 2 23 340 012 2 240123450 5 10 15 20 25 30 35 40Number of creep voids inan imageImage numberground truthpredictedFig. 4. Number of creep voids in an image, ground truth vs. predictedFig. 2. Comparison between ground truth and predicted masks

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Semantic segmentation for the analysis of creep voids in metallic materials

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