- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
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- Chinese Science Citation Database
| Citation: | CAO Yungang, ZENG Yahui, CHENG Haibo, SUI Baikai, ZHAO Jun, PAN Rumeng. Multi-view Method for Forest Fire Detection Based on Omni-Dimensional Dynamic Convolution and Focal-IoU[J]. Journal of Southwest Jiaotong University, 2026, 61(1): 151-159. doi: 10.3969/j.issn.0258-2724.20240229
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Forest fire detection is crucial for forest fire emergency rescue. To address the shortcomings of existing models in sample quality, multi-scale issues, and generalization capability across multi-view images, a method for forest fire detection based on YOLO (FFD-YOLO) was proposed. First, a multi-view visible light image dataset for detecting forest fire from of high point view (FFHPV) was constructed to enhance the model’s learning capability for multi-view fire information. Second, omni-dimensional dynamic convolution was introduced to develop an omni-dimensional spatial pyramid pooling (OD-SPP) to improve the model’s feature extraction capacity for multi-view fire characteristics. Finally, a wise intersection over union (Wise-IoU) loss function with a dynamic non-monotonic focusing mechanism was introduced to mitigate the impact of low-quality data on model precision and enhance small-target fire detection. Experimental results have demonstrated that FFD-YOLO increased precision by 3.9%, recall by 3.7%, mean average precision (mAP) by 4.0%, and F1-score by 0.038 compared to YOLOv7. In comparative experiments with YOLOv5, YOLOv8, dense distinct query (DDQ), detection transformer with improved denoising anchor boxes (DINO), Faster R-CNN, Sparse R-CNN, Mask R-CNN, FCOS, and YOLOX, FFD-YOLO attained 75.3% precision, 73.8% recall, 77.6% mAP, and 0.745 F1-score, validating its feasibility and effectiveness.
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