WRICE JSON TO YOLO

## Overview

WRICE JSON TO YOLO is a dataset for instance segmentation tasks - it contains WeedyRice annotations for 608 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains 1660 images of electric substations with 50705 annotated objects. The images were obtained using different cameras, including cameras mounted on Autonomous Guided Vehicles (AGVs), fixed location cameras and those captured by humans using a variety of cameras. A total of 15 classes of objects were identified in this dataset, and the number of instances for each class is provided in the following table:

Object classes and how many times they appear in the dataset.


    Class
    Instances




    Open blade disconnect
    310


    Closed blade disconnect switch
    5243


    Open tandem disconnect switch
    1599


    Closed tandem disconnect switch
    966


    Breaker
    980


    Fuse disconnect switch
    355


    Glass disc insulator
    3185


    Porcelain pin insulator
    26499


    Muffle
    1354


    Lightning arrester
    1976


    Recloser
    2331


    Power transformer
    768


    Current transformer
    2136


    Potential transformer
    654


    Tripolar disconnect switch
    2349

All images in this dataset were collected from a single electrical distribution substation in Brazil over a period of two years. The images were captured at various times of the day and under different weather and seasonal conditions, ensuring a diverse range of lighting conditions for the depicted objects. A team of experts in Electrical Engineering curated all the images to ensure that the angles and distances depicted in the images are suitable for automating inspections in an electrical substation.

The file structure of this dataset contains the following directories and files:

images: This directory contains 1660 electrical substation images in JPEG format.

images: This directory contains 1660 electrical substation images in JPEG format.

labels_json: This directory contains JSON files annotated in the VOC-style polygonal format. Each file shares the same filename as its respective image in the images directory.

15_masks: This directory contains PNG segmentation masks for all 15 classes, including the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.

14_masks: This directory contains PNG segmentation masks for all classes except the porcelain pin insulator. Each file shares the same name as its corresponding image in the images directory.

porcelain_masks: This directory contains PNG segmentation masks for the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory.

classes.txt: This text file lists the 15 classes plus the background class used in LabelMe.

json2png.py: This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations.

The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. The dataset is expected to be useful for researchers, practitioners, and engineers interested in developing and testing object detection and segmentation models for automating inspection and maintenance activities in electrical substations.

The authors would like to thank UTFPR for the support and infrastructure made available for the development of this research and COPEL-DIS for the support through project PD-2866-0528/2020—Development of a Methodology for Automatic Analysis of Thermal Images. We also would like to express our deepest appreciation to the team of annotators who worked diligently to produce the semantic labels for our dataset. Their hard work, dedication and attention to detail were critical to the success of this project.

Note: This dataset was captured by UG EEE students at the University of Strathclyde.

Overview

This dataset contains 26 uncalibrated hyperspectral images captured at Strathclyde University. Each image is provided with hyperspectral and annotation data. Seven material classes have been used to label the dataset: artificial, rock, sky, soil, water, wood, vegetation.

For each sample, the following files are included:

• image.npy – a NumPy array containing the hyperspectral data with shape (800, 800, 600).

• image.json – annotations created using LabelMe, describing object masks and regions of interest.

• baslerPIA1600_calibration.txt – a text file containing the wavelengths corresponding to each of the 600 spectral bands captured by the camera. These values are crucial for interpreting the spectral dimension of the data.

Convert JSON to Annotated Image

You can convert the `.json` annotations into masks and visualisations using the LabelMe tools.

Install labelme if you haven't:

pip install labelme

Run the following command inside a sample directory:

labelme_json_to_dataset image.json

This will generate a folder image_json/ containing:

• label.png – a PNG mask of labels

• label_viz.png – a visual representation of the annotation

• label_names.txt – list of class labels

Collection of annotated truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.

The dataset includes 727 cropped images of trucks, taken with three different cameras, on five different locations.

727 images

Format: JPG

Resolution: 1920xVarious, 96dpi, 24bits

Naming pattern: _--.jpg

All annotated objects were created with LabelMe, and saved in JSON files for each image. For more information about the annotation format, please refer to the LabelMe documentation.

Annotated objects are all related to truck axles, in 4 categories, Truck, Axle, Tandem, Tridem. Tandem is a double axle composition, and tridem is a triple axle composition. The number of objects in each category is as follows:

Truck: 736

Axle: 2711

Tandem: 809

Tridem: 130

If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!

Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).

The image data were collect from the department of ultrasond of the 906th hospitalof. These ultrasound thyroid images are showing the thyroid noduals labeled by labelme program. One image with a json file with same name, which have the label information.

Melt pool images with related annotations in LW-DED processes with Stainless Steel (SS) and low carbon steel (S355) substrates and Inconel 625 wire material are provided for instance segmentation methods.

The annotation of all images in the dataset was carried out utilizing the "labelme" library, which is a Python package. This library offers an image annotation tool and various functionalities for reading, writing, and converting the resulting annotations in JSON format.

This dataset collected a total of 2500 remote sensing images of rivers in mountainous areas at the municipal level and downstream river mouths in Fujian Province in 2024 (with a spatial range roughly between 23 ° 30'N-28 ° 20'N and 115 ° 50'E-120 ° 40'E), covering remote sensing images of rivers at different altitudes, terrain, widths, and curvatures (with a spatial resolution of 500 meters). The pixel values of the image are all 1000 × 1000, and Labelme standard software is used to segment and label the water areas in the image. The dataset contains four subfolders, which respectively store the original data image, JSON format labels, mask label images, and visual overlay images.

This dataset contains 1660 images of electric substations with 50705 annotated objects. The images were obtained using different cameras, including cameras mounted on Autonomous Guided Vehicles (AGVs), fixed location cameras and those captured by humans using a variety of cameras. A total of 15 classes of objects were identified in this dataset. These are the classes and their number of instances:

Open blade disconnect switch: 310 Closed blade disconnect switch: 5243 Open tandem disconnect switch: 1599 Closed tandem disconnect switch: 966 Breaker: 980 Fuse disconnect switch: 355 Glass disc insulator: 3185 Porcelain pin insulator: 26499 Muffle: 1354 Lightning arrester: 1976 Recloser: 2331 Power transformer: 768 Current transformer: 2136 Potential transformer: 654 Tripolar disconnect switch: 2349

All images in this dataset were collected from a single electrical distribution substation in Brazil over a period of two years. The images were captured at various times of the day and under different weather and seasonal conditions, ensuring a diverse range of lighting conditions for the depicted objects. A team of experts in Electrical Engineering curated all the images to ensure that the angles and distances depicted in the images are suitable for automating inspections in an electrical substation.

The file structure of this dataset contains the following directories and files:

images: This directory contains 1660 electrical substation images in JPEG format. labels_json: This directory contains JSON files annotated in the VOC-style polygonal format. Each file shares the same filename as its respective image in the images directory. 15_masks: This directory contains PNG segmentation masks for all 15 classes, including the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory. 14_masks: This directory contains PNG segmentation masks for all classes except the porcelain pin insulator. Each file shares the same name as its corresponding image in the images directory. porcelain_masks: This directory contains PNG segmentation masks for the porcelain pin insulator class. Each file shares the same name as its corresponding image in the images directory. classes.txt: This text file lists the 15 classes plus the background class used in LabelMe. json2png.py: This Python script can be used to generate segmentation masks using the VOC-style polygonal JSON annotations.

The dataset aims to support the development of computer vision techniques and deep learning algorithms for automating the inspection process of electrical substations. We expect it to be useful for researchers, practitioners, and engineers interested in developing and testing object detection and segmentation models for automating inspection and maintenance activities in electrical substations.