# Result Photovoltaic fault detector

## Panel Detector
### SDD7
On folder [Result ssd7 panel](Result_ssd7_panel/) show code (jupyter notebook), weight and result of this model (mAP 89.8%).

<p align="center">
  <img width="800" height="400" src="Result_ssd7_panel/result_ssd7_panel/DJI_0020.jpg">
</p>
<p align="center">
  <img width="800" height="400" src="Result_ssd7_panel/result_ssd7_panel/DJI_0110.jpg">
</p>



### YOLO3
On folder [Result yolo3 panel](Result_yolo3_panel/)  weight and result of this model (mAP 86.3%).

<p align="center">
  <img width="460" height="300" src="Result_yolo3_panel/Mision%203_DJI_0045.jpg">
</p>



## Soiling Fault Detector
### SSD300
On folder [Result ssd300 fault 1](Result_ssd300_fault_1/) show code (jupyter notebook), weight and result of this model (mAP 79.5%).

<p align="center">
  <img width="800" height="400" src="Result_ssd300_fault_1/result_ssd300_fault_1/Mision_11_DJI_0011.jpg">
</p>


### YOLO3
On folder [Result yolo3 fault 1](Result_yolo3_fault_1/) show [history train](Result_yolo3_fault_1/yolo3_full_yolo.output), weight and result of this model (mAP 73.02%).

<p align="center">
  <img width="460" height="300" src="Result_yolo3_fault_1/result_yolo3_fault_1/Mision_11_DJI_0011.jpg">
</p>


## Affected Cell Detector
### YOLO3
On folder [Result yolo3 fault 2](Result_yolo3_fault_2/) show [history train](Result_yolo3_fault_2/yolo3_full_yolo.output), weight and result of this model (mAP 71.93%).


<p align="center">
  <img width="460" height="300" src="Result_yolo3_fault_2/result_yolo3_fault_2/Mision%2010_DJI_0093.jpg">
</p>


## Diode Fault Detector
### YOLO3
On folder [Result yolo3 fault 4](Result_yolo3_fault_4/) show [history train](Result_yolo3_fault_4/yolo3_full_yolo.output), weight and result of this model (mAP 66.22%).


<p align="center">
  <img width="460" height="300" src="Result_yolo3_fault_4/result_yolo3_fault_4/Mision%2041_DJI_0044.jpg">
</p>





## Panel Disconnect Detector
### YOLO3
To use the detector we must only use 'panel_yolo3_disconnect.py' with the previously established form, that is:
`python predict_yolo3_disconnect.py -c config_full_yolo_panel_infer.json -i /path/to/image/ -o /path/output/result`
To use this model, only the yolo3_panel detector model is needed.

<p align="center">
  <img width="460" height="300" src="Result_yolo3_panel/Mision%2011_DJI_0058.jpg">
</p>


The idea to detect the disconnection is by calculating the luminosity of each panel, to then normalize this data and highlight the panels with a luminosity out of normality.