基于动态形状特征提取及增强的改进YOLOv3火焰检测算法

Improved YOLOv3 Flame Detection Algorithm Based on Dynamic Shape Feature Extraction and Enhancement

针对现有多目标检测网络对动态火焰特征提取及增强能力不足,检测效果不佳的问题,提出基于动态形状特征提取及增强的改进YOLOv3火焰检测算法。采用小尺寸结构的ResNet50_vd作为YOLOv3的主干网络,减少特征信息冗余;在主干网络stage 4和stage 5中加入可变形卷积模块,控制采样网格随火焰目标形状的动态变化;引入交并比(IoU)Aware模块,增加置信度得分与IoU定位精度的相关性,提高网络的火焰特征提取能力;同时在YOLOv3 Head中加入Drop Block,引入IoU预测分量优化损失函数,提高模型学习过程中的特征增强能力。通过消融实验验证各改进部分对模型的提升效果,实验结果表明,改进模型对火焰的检测精度达94.11%,推理速度达73.52 frame/s,能够有效满足对动态形状火焰的检测要求。

To address the issue that the existing multitarget detection network cannot extract and enhance dynamic flame features,resulting in poor detection results,this paper presents an improved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement.ResNet50_vd with a small size structure is used as the backbone network of YOLOv3 to reduce the redundancy of feature information.To control the dynamic change of the sampling grid with the shape of the flame target,deformable convolutional neural network modules are added to the backbone network stage 4 and stage 5.The IoU Aware module is introduced to increase the correlation between the confidence score and the positioning accuracy of the IoU,and to enhance the flame feature extraction ability of the network.Simultaneously,the Drop Block module is added to the YOLOv3 Head,and the IoU prediction component is introduced to optimize the loss function,which improves the feature enhancement ability during the model learning process.The ablation experiments were performed to verify the effect of each improvement on the proposed model.The results show that the improved model for flame detection has a detection accuracy of 94.11%and an inference speed of 73.52 frame/s,which can effectively meet the detection requirements of dynamic shape flames.