Data

Annotated Fire -Smoke Image Dataset for fire detection Using YOLO.

Central Queensland University
Shouthiri Partheepan (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25946/28747046.v1&rft.title=Annotated Fire -Smoke Image Dataset for fire detection Using YOLO.&rft.identifier=https://doi.org/10.25946/28747046.v1&rft.publisher=Central Queensland University&rft.description=This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.&rft.creator=Shouthiri Partheepan&rft.date=2025&rft_rights=CC-BY-4.0&rft_subject=Fire detection&rft_subject=smoke detection&rft_subject=YOLO annotations&rft_subject=UAV fire monitoring&rft_subject=YOLO&rft_subject=Planning and decision making&rft_subject=Deep learning&rft_subject=Semi- and unsupervised learning&rft.type=dataset&rft.language=English Access the data
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This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

Issued: 2025-04-14

Created: 2025-04-14

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Subjects
Deep learning | Fire detection | Planning and decision making | Semi- and unsupervised learning | UAV fire monitoring | YOLO | YOLO annotations | smoke detection |

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