% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Voelter:641395,
      author       = {Voelter, Constantin and Starostin, Vladimir and Lapkin,
                      Dmitrii and Munteanu, Valentin and Romodin, Mikhail and
                      Hylinski, Maik and Gerlach, Alexander and Hinderhofer,
                      Alexander and Schreiber, Frank},
      title        = {{B}enchmarking deep learning for automated peak detection
                      on {GIWAXS} data},
      journal      = {Journal of applied crystallography},
      volume       = {58},
      number       = {2},
      issn         = {0021-8898},
      address      = {Copenhagen},
      publisher    = {Munksgaard},
      reportid     = {PUBDB-2025-05056},
      pages        = {513 - 522},
      year         = {2025},
      abstract     = {Recent advancements in X-ray sources and detectors have
                      dramatically increased data generation, leading to a greater
                      demand for automated data processing. This is particularly
                      relevant for real-time grazing-incidence wide-angle X-ray
                      scattering (GIWAXS) experiments which can produce hundreds
                      of thousands of diffraction images in a single day at a
                      synchrotron beamline. Deep learning (DL)-based
                      peak-detection techniques are becoming prominent in this
                      field, but rigorous benchmarking is essential to evaluate
                      their reliability, identify potential problems, explore
                      avenues for improvement and build confidence among
                      researchers for seamless integration into their workflows.
                      However, the systematic evaluation of these techniques has
                      been hampered by the lack of annotated GIWAXS datasets,
                      standardized metrics and baseline models. To address these
                      challenges, we introduce a comprehensive framework
                      comprising an annotated experimental dataset,
                      physics-informed metrics adapted to the GIWAXS geometry and
                      a competitive baseline – a classical, non-DL
                      peak-detection algorithm optimized on our dataset.
                      Furthermore, we apply our framework to benchmark a recent DL
                      solution trained on simulated data and discover its superior
                      performance compared with our baseline. This analysis not
                      only highlights the effectiveness of DL methods for
                      identifying diffraction peaks but also provides insights for
                      further development of these solutions.},
      cin          = {DOOR ; HAS-User},
      ddc          = {540},
      cid          = {I:(DE-H253)HAS-User-20120731},
      pnm          = {6G3 - PETRA III (DESY) (POF4-6G3) / DFG project
                      G:(GEPRIS)390727645 - EXC 2064: Maschinelles Lernen: Neue
                      Perspektiven für die Wissenschaft (390727645) / DFG project
                      G:(GEPRIS)460248799 - DAPHNE4NFDI - DAten aus PHoton- und
                      Neutronen Experimenten für NFDI (460248799) / 05K19VTA -
                      Entwicklung einer kompakten Probenumgebung mit Spin-Coater
                      für in-situ Röntgenstreuung an PETRA III. (BMBF-05K19VTA)},
      pid          = {G:(DE-HGF)POF4-6G3 / G:(GEPRIS)390727645 /
                      G:(GEPRIS)460248799 / G:(DE-Ds200)BMBF-05K19VTA},
      experiment   = {EXP:(DE-H253)P-P08-20150101},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1107/S1600576725000974},
      url          = {https://bib-pubdb1.desy.de/record/641395},
}