Rentadrone_MachineLearning Photovoltaic fault detector
To do list:
- Import model detection (SSD & YOLO3)
- Model Panel Detection (SSD7)
- Model Panel Detection (YOLO3)
- Model Soiling Fault Detection (YOLO3)
- Model Diode Fault Detection (YOLO3)
- Model Other Fault Detection
- Model Fault Panel Disconnect
- Example use Trained Model
Dependencies
- Python 3.x
- Numpy
- TensorFlow 2.x
- Keras 2.x (in TensorFlow)
- OpenCV
- Beautiful Soup 4.x
Model Detection
The models used for detection are SSD SSD: Single Shot MultiBox Detector and YOLOv3 [YOLOv3: An Incremental Improvement] (https://arxiv.org/abs/1804.02767), they are imported from the following repositories:
Grab the pretrained weights of SSD and YOLO3 from Drive_Weights
| Model | Pretrained Weights |
|---|---|
| SSD7/SSD300 | Weight VGG16 |
| YOLO3 | Weight Full Yolo3 |
Type of Data
The images used for the design of this model were extracted by air analysis, specifically: FLIR aerial radiometric thermal infrared pictures, taken by UAV (R-JPEG format). Which were converted into .jpg images for the training of these detection models. Example FLIR image:
Same image in .jpg format:
Training
1. Data preparation
View folder Train&Test_A/ and Train&Test_S/, example of panel anns and soiling fault anns.
Organize the dataset into 4 folders:
-
train_image_folder <= the folder that contains the train images.
-
train_annot_folder <= the folder that contains the train annotations in VOC format.
-
valid_image_folder <= the folder that contains the validation images.
-
valid_annot_folder <= the folder that contains the validation annotations in VOC format.
There is a one-to-one correspondence by file name between images and annotations. For create own data set use LabelImg code from : https://github.com/tzutalin/labelImg
2. Edit the configuration file
The configuration file for YOLO3 is a json file, which looks like this (example soiling fault ):
{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["1"],
"backend": "full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_S/Train/images/",
"train_annot_folder": "../Train&Test_S/Train/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/experimento_fault_1_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "log_experimento_fault_gpu",
"saved_weights_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/experimento_yolo3_full_fault.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_S/Test/images/",
"valid_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/val_fault_1.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_S/Test/images/",
"test_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/test_fault_1.pkl",
"test_times": 1
}
}
The configuration file for SSD300 is a json file, which looks like this (example soiling fault ) and .txt with name of images (train.txt):
{
"model" : {
"backend": "ssd300",
"input": 400,
"labels": ["1"]
},
"train": {
"train_image_folder": "Train&Test_S/Train/images",
"train_annot_folder": "Train&Test_S/Train/anns",
"train_image_set_filename": "Train&Test_S/Train/train.txt",
"train_times": 1,
"batch_size": 12,
"learning_rate": 1e-4,
"warmup_epochs": 3,
"nb_epochs": 100,
"saved_weights_name": "Result_ssd300_fault_1/experimento_ssd300_fault_1.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_D/Test/images/",
"valid_annot_folder": "../Train&Test_D/Test/anns/",
"valid_image_set_filename": "../Train&Test_D/Test/test.txt"
},
"test": {
"test_image_folder": "Train&Test_S/Test/images",
"test_annot_folder": "Train&Test_S/Test/anns",
"test_image_set_filename": "Train&Test_S/Test/test.txt"
}
}
3. Start the training process
python train_ssd.py -c config.json -o /path/to/result
or
python train_ssd.py -c config.json -o /path/to/result
By the end of this process, the code will write the weights of the best model to file best_weights.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file). The training process stops when the loss on the validation set is not improved in 20 consecutive epoches.
4. Perform detection using trained weights on image, set of images
python predict_ssd.py -c config.json -i /path/to/image/or/video -o /path/output/result
or
python predict_yolo.py -c config.json -i /path/to/image/or/video -o /path/output/result
It carries out detection on the image and write the image with detected bounding boxes to the same folder.
Evaluation
The evaluation is integrated into the training process, if you want to do the independent evaluation you must go to the folder ssd_keras-master or keras-yolo3-master and use the following code
python evaluate.py -c config.json
Compute the mAP performance of the model defined in saved_weights_name on the validation dataset defined in valid_image_folder and valid_annot_folder.
Result
All of weights of this trained model grab from Drive_Weights
| Model | Weights Trained | Config |
|---|---|---|
| SSD7 Panel | weight | config |
| SSD300 Soiling | weight | config |
| YOLO3 Panel | weight | config |
| YOLO3 Soiling | weight | config |
| YOLO3 Diode | weight | config |
| YOLO3 Affected Cell | [weight(https://drive.google.com/open?id=1ngyCzw7xF0N5oZnF29EIS5LOl1PFkRRM) | config |
Panel Detector
SDD7
On folder Result ssd7 panel show code (jupyter notebook), weight and result of this model (mAP 89.8%).
YOLO3
On folder Result yolo3 panel weight and result of this model (mAP 86.3%).
Soiling Fault Detector
SSD300
On folder Result ssd300 fault 1 show code (jupyter notebook), weight and result of this model (mAP 79.5%).
YOLO3
On folder Result yolo3 fault 1 show history train, weight and result of this model (mAP 73.02%).
Diode Affected Cell Detector
YOLO3
On folder Result yolo3 fault 2 show history train, weight and result of this model (mAP 71.93%).
Diode Fault Detector
YOLO3
On folder Result yolo3 fault 4 show history train, weight and result of this model (mAP 66.22%).
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.
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.
Contributing
Contributions are welcome and will be fully credited. We accept contributions via Pull Requests on GitHub.
Pull Request Checklist
Before sending your pull requests, make sure you followed this list.
- Read contributing guidelines.
- Read Code of Conduct.
- Check if my changes are consistent with the guidelines.
- Changes are consistent with the Coding Style.
- Run Unit Tests.
Example to use trained model
In 'Example_Prediction' this is the example of how to implement an already trained model, it can be modified to change the model you have to use and the image in which you want to detect faults. In ['Example_Prediction_AllInOne'](Example Detection AllInOne.ipynb) this is the example of how implement all trained model, you can use this code for predict a folder of images and have a output image with detection boxes.