西南交通大学学报2026，Vol.61Issue(1)：147-155,9.DOI:10.3969/j.issn.0258-2724.20240229
基于全维动态卷积与聚焦IoU的多视角森林火点检测方法
Multi-view Method for Forest Fire Detection Based on Omni-Dimensional Dynamic Convolution and Focaler-IoU
摘要
Abstract
Forest fire detection is crucial for forest fire emergency rescue.To address the shortcomings of existing models in sample quality,multi-scale object detection 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 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 achieves the best results with 75.3%precision,73.8%recall,77.6%mAP,and 0.745 F1-score,validating its feasibility and effectiveness.关键词
森林火点检测/多视角图像/全维动态卷积/聚焦IoU/目标检测Key words
forest fire detection/multi-view image/omni-dimensional dynamic convolution/focaler-IoU/target detection分类
农业科技引用本文复制引用
曹云刚,曾雅慧,程海波,隋百凯,赵俊,潘如梦..基于全维动态卷积与聚焦IoU的多视角森林火点检测方法[J].西南交通大学学报,2026,61(1):147-155,9.基金项目
国家重点研发计划(2022YFC3005703) （2022YFC3005703）
