基于全维动态卷积与聚焦IoU的多视角森林火点检测方法OA

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.