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Key message <p>The Biomtrace database contains 33,390 RGB images of the butt end cross-section of 5135 French oak logs. Each log was photographed several times with different camera orientations during an initial shooting session. For more than half of the logs, additional photos were taken at least 3 weeks after the first photo session. Cross-sections were segmented on all the images using the PointRend convolutional neural network. Spatial calibration was carried out by a specific algorithm using a checkerboard pattern present in each image. This image database was created with the aim of developing algorithms for the biometric traceability of logs, involving artificial intelligence approaches that require large databases. But other applications are also possible, such as the automatic extraction of information on the size and quality of logs.&#xa0;The Biomtrace database is available at <a href="https://doi.org/10.57745/9DBCL4">https://doi.org/10.57745/9DBCL4</a>, and associated metadata are available at <a href="https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/feda0a0e-041a-4190-9a73-5159b10ff0f0">https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/feda0a0e-041a-4190-9a73-5159b10ff0f0</a>.</p>

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Traceability of oak (Quercus petraea (Matt.) Liebl. and Quercus robur L.) logs: the Biomtrace database

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月17日

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  • Fleur Longuetaud,
  • Frédéric Mothe,
  • Dorian Martinetto,
  • Phuc Ngo,
  • Alexandre Piboule,
  • Daniel Rittié,
  • Frédéric Bordat,
  • Philippe Jacquin,
  • Isabelle Debled-Rennesson,
  • Aymeric Albert,
  • Claudine Richter

摘要

Key message

The Biomtrace database contains 33,390 RGB images of the butt end cross-section of 5135 French oak logs. Each log was photographed several times with different camera orientations during an initial shooting session. For more than half of the logs, additional photos were taken at least 3 weeks after the first photo session. Cross-sections were segmented on all the images using the PointRend convolutional neural network. Spatial calibration was carried out by a specific algorithm using a checkerboard pattern present in each image. This image database was created with the aim of developing algorithms for the biometric traceability of logs, involving artificial intelligence approaches that require large databases. But other applications are also possible, such as the automatic extraction of information on the size and quality of logs. The Biomtrace database is available at https://doi.org/10.57745/9DBCL4, and associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/feda0a0e-041a-4190-9a73-5159b10ff0f0.