Fire_detection_version_yolov11

This dataset is developed to support research in vision-based fire and smoke detection using deep learning models, particularly YOLO-based architectures. It contains a curated collection of images representing three classes: fire, smoke, and background.

The fire class includes visible flame scenarios under diverse environmental conditions. The smoke class contains different smoke patterns, including light haze and dense smoke, with or without visible flames. The background class consists of normal scenes and hard negative samples such as sunlight reflections, clouds, fog, steam, dust, and artificial lighting, which are commonly misclassified as fire or smoke.

The dataset is constructed from multiple sources to ensure diversity and robustness. A portion of the images is derived from publicly available datasets , including:- Peng, B., & Kim, T.-K. (2025). YOLO-HF: Early Detection of Home Fires Using YOLO. IEEE Access, 13, 79451–79466.- Putra, A. K. (2025). Indoor Fire Smoke Dataset.

In addition, supplementary images of fire, smoke, and non-fire scenes were collected from open-access platforms, including Pixabay, Wikimedia Commons, and Pexels.

All images are annotated in YOLO format using bounding boxes. Where applicable, polygon annotations were converted into bounding boxes during preprocessing to ensure compatibility with object detection frameworks.

The dataset is divided into training, validation, and test sets using a 70/15/15 split. Data augmentation techniques were applied to the training set to improve generalization, including rotation, cropping, shearing, and color adjustments.

The dataset was iteratively refined across multiple preprocessing versions to improve annotation quality and reduce noise. It is specifically designed to address the false positive problem in fire detection by including challenging negative samples.

In addition to the image data, this repository provides annotation files and dataset splits to support reproducibility.