Scholarlink

<p>Object detection methods based on deep learning have significantly reduced time-consuming tasks. Semantic segmentation has shown remarkable progress in the study of rocks, especially when applied to petrographic thin sections. Despite the development of various models for specific applications in this field with promising results, their widespread adoption remains limited. This hesitation is largely due to a lack of user confidence stemming from the absence of explainability in the outcomes provided by these models. This study explores the explainability of the state-of-the-art YOLOv11 model in detecting andalusite, biotite, and grains with oolitic textures. We trained three models using plane-polarized-light thin-section microphotographs of the selected targets. Subsequently, we applied color and singular value perturbations to the annotated images using color masks and analyzed the model’s inference through connected region heatmaps. Our findings suggest that the trained models prioritize low-frequency attributes like shape, predominant colors, and contrast over the studied targets’ characteristic tones. These insights contribute to the practical application of deep learning for detecting and segmenting grains and minerals in thin sections.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Visual analysis of deep learning semantic segmentation applied to petrographic thin sections

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月26日

查看全文
  • Joaquin Morales,
  • Camila Saldivia,
  • Rodolfo Lobo,
  • Max del Pino,
  • Marcos Muñoz,
  • Giorgio Caniggia,
  • Joaquín Catalán,
  • Rafael Hayde,
  • Víctor Poblete

摘要

Object detection methods based on deep learning have significantly reduced time-consuming tasks. Semantic segmentation has shown remarkable progress in the study of rocks, especially when applied to petrographic thin sections. Despite the development of various models for specific applications in this field with promising results, their widespread adoption remains limited. This hesitation is largely due to a lack of user confidence stemming from the absence of explainability in the outcomes provided by these models. This study explores the explainability of the state-of-the-art YOLOv11 model in detecting andalusite, biotite, and grains with oolitic textures. We trained three models using plane-polarized-light thin-section microphotographs of the selected targets. Subsequently, we applied color and singular value perturbations to the annotated images using color masks and analyzed the model’s inference through connected region heatmaps. Our findings suggest that the trained models prioritize low-frequency attributes like shape, predominant colors, and contrast over the studied targets’ characteristic tones. These insights contribute to the practical application of deep learning for detecting and segmenting grains and minerals in thin sections.