# DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-convolutional-neural-network-for-automatic-detection-of-damaged/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Roberto Pierdicca,Eva Savina Malinverni,F. Piccinini,Marina Paolanti,Andrea Felicetti,Primo Zingaretti |
| Citations | 168 |
| DOI | 10.5194/isprs-archives-xlii-2-893-2018 |
| Fields | Computer Science,Energy,Engineering |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/893/2018/isprs-archives-XLII-2-893-2018.pdf |
| OpenAlex ID | https://openalex.org/W2806915479 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Roberto Pierdicca](https://scholariq.org/researchers/roberto-pierdicca/)
- [Marina Paolanti](https://scholariq.org/researchers/marina-paolanti/)

## Paper primary topic

- [Photovoltaic System Optimization Techniques](https://scholariq.org/topics/photovoltaic-system-optimization-techniques/)

## Paper topics

- [Photovoltaic System Optimization Techniques](https://scholariq.org/topics/photovoltaic-system-optimization-techniques/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Industrial Vision Systems and Defect Detection](https://scholariq.org/topics/industrial-vision-systems-and-defect-detection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.