| # Powerline Components and Faults Dataset | |
| ## Overview | |
| The **Powerline Components and Faults Dataset** is a dataset designed for object detection tasks involving powerline components and associated faults. It provides images of powerline infrastructure along with annotated bounding boxes for various components and faults. | |
| This dataset is augmented with mosaic augmentation useful for training and evaluating models on powerline inspection, maintenance, and safety applications. | |
| ## Dataset Structure | |
| The dataset is organized into the following directories: | |
| - `train/`: Contains training images and their corresponding annotation files. | |
| - `validation/`: Contains validation images and their corresponding annotation files. | |
| - `test/`: Contains test images and their corresponding annotation files. | |
| Each image file has a corresponding `.txt` file in the same directory, which contains the annotations in YOLO format. | |
| ## Data Format | |
| ### Images | |
| - Format: JPEG/PNG | |
| - Resolution: Various resolutions | |
| ### Annotations | |
| Annotations are provided in YOLO format, where each line in a `.txt` file corresponds to an object in the image. The format is: | |
| ``` | |
| class_id x_center y_center width height | |
| ``` | |
| - `class_id`: The ID of the object class. | |
| - `x_center`, `y_center`: The center of the bounding box (normalized between 0 and 1). | |
| - `width`, `height`: The dimensions of the bounding box (normalized between 0 and 1). | |
| ## Usage | |
| You can use this dataset with popular machine learning frameworks and libraries. Below is an example of how to load the dataset using the Hugging Face `datasets` library: | |
| ```python | |
| from datasets import load_dataset | |
| # Load the dataset | |
| dataset = load_dataset("docmhvr/powerline-components-and-faults") | |
| # Access the train, validation, and test splits | |
| train_dataset = dataset['train'] | |
| val_dataset = dataset['validation'] | |
| test_dataset = dataset['test'] | |
| ``` | |
| ## License | |
| This dataset is provided under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details. | |
| ## Acknowledgements | |
| This dataset was created as part of the research work on powerline inspection and fault detection. Data was collected using DJI Mini drone and manually compiled and annotated using Roboflow. | |
| ## Research reference | |
| You can find the related Research work published in IEEE, full text avaliable on researchgate here, | |
| [Research Paper](https://www.researchgate.net/publication/381461493_UAV-Based_Powerline_Problem_Inspection_and_Classification_using_Machine_Learning_Approaches) | |
| ## Contribution | |
| If you would like to contribute to this dataset, please feel free to open an issue or submit a pull request on the [GitHub repository](https://github.com/docmhvr/UAV-Based-Powerline-Problem-Inspection-Using-Machine-Learning). | |