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:

• SSD_Keras
• YOLOv3_Keras

Grab the pretrained weights of SSD and YOLO3 from Drive_Weights

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

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 Fault Detector

YOLO3

On folder Result yolo3 fault 4 show history train, weight and result of this model (mAP 73.02%).

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.