DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Pierdicca, R.^1¹  Malinverni, E. S.^1² 

[1] Universitá Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’Architettura, 60100 Ancona, Italy^1;Universitá Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione, 60100 Ancona, Italy^2

关键词: UAV;    monitoring/inspection;    Anomaly Detection;    Deep Learning;    Photovoltaic Panels;    Thermography;    Classification;   

DOI  :  10.5194/isprs-archives-XLII-2-893-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY