Photovoltaic Fault Detector

SimpleMap.io

Forum

This project is part of the UNICEF Innovation Fund Discourse community. You can post comments or questions about each category of SimpleMap.io Open-Source Initiative algorithms. We encourage users to participate in the forum and to engage with fellow users.

Summary

Model-definition is a deep learning application for fault detection in photovoltaic plants. In this repository you will find trained detection models that point out where the panel faults are by using radiometric thermal infrared pictures. In Web-API contains a performant, production-ready reference implementation of this repository.

To do list:

• Import model detection (SSD & YOLO3)
• Example use Trained Model
• Train and Evaluate Model with own data
• 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

Requirements

• Python 3.x
• Numpy
• TensorFlow 2.x
• Keras 2.x (in TensorFlow)
• OpenCV
• Beautiful Soup 4.x

Quickstart

In the root project execute the following command to install all dependencies project

pip install -r requirements.txt

You need install Jupyter notebook to see the code example. You can find the installation documentation for the Jupyter platform, on ReadTheDocs or in github page here.

For a local installation, make sure you have pip installed and run:

pip install notebook

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' 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.

In 'Example_Prediction_Orthophoto' this is the example of how implement all trained model, you can use this code for predict a Orthophot and have a output image with detection boxes.

Developers

Help improve our software! We welcome contributions from everyone, whether to add new features, improve speed, fix existing bugs or add support. Check our code of conduct, the contributing guidelines and how decisions are made.

Any code contributions are welcomed as long as they are discussed in Github Issues with maintainers. Be aware that if you decide to change something and submit a PR on your own, it may not be accepted.

Creating an issue

You can open a new issue based on code from an existing pull request. For more information, see the template for filling issues

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

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_yolo.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 Example: python keras-yolo3-master/evaluate.py -c config_full_yolo_fault_1_infer.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.

Model	mAP	Config
YOLO3 Soiling	0.7302	config
YOLO3 Diode	0.6127	config
YOLO3 Affected Cell	0.7230	config

Weights of Trained Models

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	config

The image used are specified in Table images. You can see some examples in Summary of results.

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.