Dataset

This dataset was created by ITK8191

Released under Apache 2.0

Contents

SemanticSugarBeets is a comprehensive dataset and framework designed for analyzing post-harvest and post-storage sugar beets using monocular RGB images. It supports three key tasks: instance segmentation to identify and delineate individual sugar beets, semantic segmentation to classify specific regions of each beet (e.g., damage, soil adhesion, vegetation, and rot) and oriented object detection to estimate the size and mass of beets using reference objects. The dataset includes 952 annotated images with 2,920 sugar-beet instances, captured both before and after storage. Accompanying the dataset is a demo application and processing code, available on GitHub. For more details, refer to the paper presented at the Agriculture-Vision Workshop at CVPR 2025.

Annotations and Learning Tasks

The dataset supports three primary learning tasks, each designed to address specific aspects of sugar-beet analysis:

1. Instance Segmentation
   Detect and delineate entire sugar-beet instances in an image. This task provides coarse-grained annotations for identifying individual beets, which is useful for counting and localization.
   
   
2. Semantic Segmentation
   Perform fine-grained segmentation of each beet instance to classify its regions into specific categories relevant to quality assessment, such as:
   • Beet: healthy, undamaged beet surfaces
   • Cut: areas where the beet has been topped or trimmed
   • Leaf: residual vegetation attached to the beet
   • Soil: soil adhering to the beet's surface
   • Damage: visible damage on the beet
   • Rot: areas affected by rot
     
     
3. Oriented Object Detection
   Detect and estimate the position and orientation of reference objects (folding-ruler elements and plastic signs) within the image. These objects can be used for scale estimation to calculate the absolute size and mass of sugar beets.

Data Structure and Formats

The dataset is organized into the following directories:

• images: contains all RGB images in .jpg format with a resolution of 2120x1192 pixels, which correspond to the annotations in the instances and markers directories
  
  
• instances: annotations and split files used in instance-segmentation experiments:
  • anno: instance contours for a single sugar-beet class in YOLO11 format
  • train/val/test.txt: lists of image IDs for training, validation and testing
    
    
• markers: annotations and split files used in oriented-object-detection experiments:
  • anno: oriented-bounding-box annotations for two classes of markers in YOLO11 format:
    • 0: Ruler (folding-ruler element)
    • 1: Sign (numbered plastic sign)
  • train/val/test.txt: lists of image IDs for training, validation and testing
    
    
• segmentation: annotations, image patches and split files used in semantic-segmentation experiments:
  • anno: single-channel segmentation masks for each individual beet, where pixel values correspond to the following classes:
    • 0: Background
    • 1: Beet
    • 2: Cut
    • 3: Leaf
    • 4: Soil
    • 5: Damage
    • 6: Rot
  • patches: image patches of individual sugar-beet instances cropped from the original images for convenience
  • train/val/test.txt: lists of beet IDs for training, validation, and testing

File Naming Convention

File names of images and annotations follow this format:

ssb-

• : a 5-digit number (e.g., 00001) identifying the group of recorded sugar beets
• : either a or b, indicating the same group of beets captured before (a) or after flipping (b)
• : a 3-digit number (e.g., 001) enumerating individual sugar beets within an image (used only for semantic segmentation)

Example

• ssb-00001a: group ID 00001, side a
• ssb-00001a-001: group ID 00001, side a, beet instance 001

Citing

If you use the SemanticSugarBeets dataset or source code in your research, please cite the following paper to acknowledge the authors' contributions:

Croonen, G., Trondl, A., Simon, J., Steininger, D., 2025. SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

D-Fire Dataset for Smoke and Fire Detection

This dataset is an enhanced version of the original D-Fire dataset, designed to facilitate smoke and fire detection tasks. It has been restructured to include a validation split, making it more accessible and user-friendly.

Explore Flare Guard

Introducing Flare Guard — an advanced, open-source solution for real-time fire and smoke detection.

This system uses YOLOv11, an advanced object detection model, to monitor live video feeds and detect fire hazards in real-time. Detected threats trigger instant alerts via Telegram and WhatsApp for rapid response.

🔗 Quick Access Links

• 📂 GitHub Repository – Access the full source code and documentation.
• 🎥 LinkedIn Project Demo & Launch – Watch the live demonstration and learn more about the project.

Example of reached Results:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12748471%2F632cfe5056cc683123c1873547d670ce%2Falert_20250210-034709-167281.jpg?generation=1742122420748481&alt=media" alt="CCVT_EXAMPLE">

Directory Structure

The dataset is organized as follows:

• train/
  • images/: Training images
  • labels/: Training labels in YOLO format
• val/
  • images/: Validation images
  • labels/: Validation labels in YOLO format
• test/
  • images/: Test images
  • labels/: Test labels in YOLO format

Classes

The dataset includes annotations for the following classes:

• 0: Smoke
• 1: Fire

Dataset Statistics

The dataset comprises over 21,000 images, categorized as follows:

Total bounding boxes:

• Fire: 14,692
• Smoke: 11,865

Data Splits

The dataset is divided into training, validation, and test sets to support model development and evaluation.

Citation

If you use this dataset in your research or projects, please cite the original paper:

Pedro Vinícius Almeida Borges de Venâncio, Adriano Chaves Lisboa, Adriano Vilela Barbosa. "An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices." Neural Computing and Applications, vol. 34, no. 18, 2022, pp. 15349–15368. DOI: 10.1007/s00521-022-07467-z.

Acknowledgments

Credit for the original dataset goes to the researchers from Gaia, solutions on demand (GAIA). The original dataset and more information can be found in the D-Fire GitHub repository.