Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 893-900, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-893-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 893-900, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-893-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

R. Pierdicca1, E. S. Malinverni1, F. Piccinini1, M. Paolanti2, A. Felicetti2, and P. Zingaretti2 R. Pierdicca et al.
  • 1Universitá Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’Architettura, 60100 Ancona, Italy
  • 22Universitá Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione, 60100 Ancona, Italy

Keywords: UAV, monitoring/inspection, Anomaly Detection, Deep Learning, Photovoltaic Panels, Thermography, Classification

Abstract. The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.