Preprints
https://doi.org/10.5194/gchron-2020-32
https://doi.org/10.5194/gchron-2020-32

  21 Oct 2020

21 Oct 2020

Review status: a revised version of this preprint is currently under review for the journal GChron.

Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

Simon Nachtergaele and Johan De Grave Simon Nachtergaele and Johan De Grave
  • Laboratory for Mineralogy and Petrology, Department of Geology, Ghent University, Ghent, 9000, Belgium

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

Simon Nachtergaele and Johan De Grave

 
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Simon Nachtergaele and Johan De Grave

Simon Nachtergaele and Johan De Grave

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Short summary
Artificial intelligence techniques are capable of detecting fission tracks automatically in minerals. The here presented AI-Track-tive software can be used to automatically determine fission track densities for apatite fission track dating studies. Apatite fission track dating is mainly applied to tectonic studies studying exhumation rates in orogens. Now, time-consuming manual track counting is replaced by deep neural networks capable of automatically finding the large majority of the tracks.