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SUMMARY:DEEP Inspection for Materials Science - Detection and Segmentation
DESCRIPTION:Register latest by: 18 September 2026\nAbout the event\nDetecting where a defect is — not just whether it exists — is the next step in developing deep learning model for real-world materials inspection. Localization and delineation of features within an image unlocks a far richer level of analysis\, whether the subject is a steel surface\, a biological tissue sample\, or a microscopy image of a crystalline material. Moving from image-level classification to precise localization and pixel-level segmentation enables a more detailed understanding of material surfaces\, supporting applications such as quality control\, failure analysis\, and process optimization in advanced manufacturing. \nThis one-day workshop focuses on object detection and instance segmentation\, equipping participants with the tools to localize and delineate multiple defects within a single image. Steel defect detection and glass fiber analysis serve as hands-on case studies\, which will provide concrete and well-annotated datasets to work with\, but the techniques and workflow are directly transferable to a wide range of scientific and industrial imaging contexts. By the end of the session\, participants will have progressed from raw annotated data to a trained\, inference-ready YOLO model within a single session. \nWho is this for?\n\n\nEngineers\, analysts\, and domain specialists who need to move beyond pass/fail classification toward precise spatial localization of features within images \n\n\nResearchers and students with basic familiarity with deep learning who want to extend their skills into detection and segmentation workflows \n\n\nKey takeaways for participants\n\n\nUnderstand the difference between classification\, object detection\, and segmentation — and when each is appropriate for inspection tasks \n\n\nLearn how YOLO works end-to-end: from bounding box prediction and confidence scoring to non-maximum suppression and real-time inference \n\n\nGain hands-on experience training YOLO from scratch and fine-tuning a pretrained YOLO model on real-world datasets. \n\n\nKnow how to prepare annotated data\, configure a training pipeline\, and evaluate and visualize model outputs \n\n\nPrerequisites\n\n\nBasic Python programming \n\n\nFamiliarity with deep learning concepts (neural networks\, training loops) — ideally from Week 1 of this workshop series or equivalent experience \n\n\nNo prior experience with object detection required \n\n\nSchedule\n\n\n\nDay\nTime\nContents\n\n\n\n\n1 October 2026\n09:00 – 12:00\nIntroduction to detection & segmentation\, detection fundamentals (bounding boxes\, IoU\, NMS)\, YOLO architecture part 1 (object detection)\, hands-on: dataset preparation & configuration\n\n\n1 October 2026\n13:00-15:30\nYOLO architecture part 2 (segmentation)\, transfer learning\, hands-on: load pretrained weights\, train YOLO\, evaluation\, inference & visualization\, buffer & wrap-up\n\n\n\n\nInstructors and Teaching Assistants\n\n\nAndreas Thore (Mimer AIF / RISE) \n\n\nSmita Chakraborty (Mimer AIF / RISE) \n\n\nMarzieh Saeedimasine (MIMER AIF/NAISS) \n\n\nRuiwen Xie (MIMER AIF/NAISS) \n\n\nYuvarajendra Anjaneya Reddy (Mimer AIF / RISE) \n\n\nYonglei Wang (MIMER AIF/NAISS)
URL:https://mimer-ai.eu/event/deep-inspection-for-materials-science-detection-segmentation/
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/Mimer-workshop.webp
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