A Benchmark Large-Size Industrial Combustion Image Dataset for Fire and Smoke Segmentation

Fire and smoke segmentation in industrial combustion scenarios is crucial for precise combustion monitoring and safety assessment. To advance research in this domain, we propose the first large-scale benchmark dataset specifically designed for semantic segmentation of fire and smoke in industrial combustion environments. The dataset comprises one hundred thousand finely annotated images extracted from real combustion videos captured across multiple factory settings, demonstrating significant environmental diversity including diverse weather conditions, varying illumination intensities, and different combustion levels. Each image is provided with pixel-level annotations. We systematically evaluate various representative deep learning segmentation architectures, presenting comprehensive performance benchmarks and comparative analysis. Experimental results not only validate the dataset's effectiveness but also establish reliable baselines for future research. This dataset is expected to make significant contributions to the advancement of fire and smoke segmentation research and its practical applications in industrial combustion scenarios.