Flammable and Explosive Object Detection: A Benchmark Dataset

The safe management of flammable and explosive objects is critical for preventing industrial accidents. However, there is currently no publicly available dataset dedicated to their automated detection. To address this gap, we present EF-Data as the primary contribution of this work—the first publicly available benchmark dataset dedicated to single-class flammable and explosive object detection, comprising 7102 images and 19,233 annotated instances across diverse industrial, commercial, and residential environments. To demonstrate the utility of EF-Data, we further propose an improved YOLOv11-based detection model incorporating the Convolutional Block Attention Module (CBAM) as a strong baseline for future research. Experimental comparisons with six state-of-the-art models demonstrate that our improved model achieves the highest mAP@0.5, yielding an absolute increase of 1.5 percentage points (from 0.885 to 0.900) over the original model, while experiencing only a marginal inference speed reduction from 191.62 to 189.43 FPS (a relative reduction of approximately 1.1%). This work contributes a dedicated benchmark and baseline model to support future research on automated flammable and explosive object detection.