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@ARTICLE{Deitsch:892292,
      author       = {Deitsch, sergiu and Buerhop-Lutz, Claudia and Sovetkin,
                      Evgenii and Steland, ansgar and Maier, Andreas and Gallwitz,
                      Florian and Riess, Christian},
      title        = {{S}egmentation of {P}hotovoltaic {M}odule {C}ells in
                      {E}lectroluminescence {I}mages},
      journal      = {Machine vision and applications},
      volume       = {32},
      issn         = {0932-8092},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2021-02001},
      pages        = {84},
      year         = {2021},
      abstract     = {High resolution electroluminescence (EL) images captured in
                      the infrared spectrum allow to visually and
                      non-destructively inspect the quality of photovoltaic (PV)
                      modules. Currently, however, such a visual inspection
                      requires trained experts to discern different kinds of
                      defects, which is time-consuming and expensive. Automated
                      segmentation of cells is therefore a key step in automating
                      the visual inspection workflow. In this work, we propose a
                      robust automated segmentation method for extraction of
                      individual solar cells from EL images of PV modules. This
                      enables controlled studies on large amounts of data to
                      understanding the effects of module degradation over
                      time—a process not yet fully understood. The proposed
                      method infers in several steps a high-level solar module
                      representation from low-level ridge edge features. An
                      important step in the algorithm is to formulate the
                      segmentation problem in terms of lens calibration by
                      exploiting the plumbline constraint. We evaluate our method
                      on a dataset of various solar modules types containing a
                      total of 408 solar cells with various defects. Our method
                      robustly solves this task with a median weighted Jaccard
                      index of $94.47\%$ and an F1 score of $97.62\%,$ both
                      indicating a high sensitivity and a high similarity between
                      automatically segmented and ground truth solar cell masks.},
      cin          = {IEK-11},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IEK-11-20140314},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000653779200001},
      doi          = {10.1007/s00138-021-01191-9},
      url          = {https://juser.fz-juelich.de/record/892292},
}