DSF-YOLO: Multi-Scale Fire and Smoke Detection Based on YOLO11

Detecting fire and smoke across a wide range of object sizes requires both spatial detail and contextual information. However, incorporating these features into a compact detector remains a challenge. This study presents DSF-YOLO, a fire and smoke detector based on YOLO11n. C3K2-MFB introduces wavelet processing into C3K2 to represent coarse structures and fine details in separate frequency subbands. DyFusion Block combines local convolutional features with transformer-based context to support complementary feature processing. ADown is also introduced to reduce the cost of downsampling. For bounding-box regression, AIM-MSAIoU combines auxiliary-box overlap at a fixed scale of 0.7 with a shape-weighted corner penalty normalized by target size. The model is evaluated on the Dual-Scale Fire (DSF) dataset, which contains 7572 images and four scale-based fire and smoke classes. Compared with YOLO11n, DSF-YOLO increases mAP@0.5 from 0.905 to 0.919 and mAP@[0.5:0.95] from 0.631 to 0.680. At the same time, parameter count decreases by 8.14% to 2.37 M, and FLOPs decrease by 4.76% to 6.0 G FLOPs. Reported throughput increases from 164.0 to 175.0 FPS on an RTX 4090D at a batch size of 16. The results show that DSF-YOLO improves detection accuracy while reducing computational cost under the reported conditions.