复杂环境下的YOLOv5s烟火检测方法

Smoke and flame detection method with YOLOv5s in complex environment

针对复杂环境中烟火检测困难,检测精度低的问题,提出一种改进YOLOv5s的烟火检测方法。首先,针对Neck层烟火特征融合不准确、效果差的问题,提出一种通道注意力机制—Scoring module,对每个通道的特征打分,选择分数高的特征进行特征融合,过滤分数低的特征,避免引入过多冗余特征,在增加少量的可训练参数情况下提高特征融合能力和检测精度;然后,为了提高Head层的预测能力,使用α-EIOU替换GIOU损失函数,提高预测框的定位和检测性能;最后,为了改善数据集数据量少、数据形式单一的问题,使用改进的Mosaic数据增强法扩充样本数据,提高模型泛化能力。实验结果表明,改进后的方法比原YOLOv5s平均精度均值高4.7%,检测速度为212 fps,同时在与其他改进型YOLOv5s的对比实验中表现较好。在环境复杂的图像中取得了较好的检测效果,可以满足复杂环境下的烟火检测任务。

Aiming at the problems of complex smoke occurrence scene and low accuracy in smoke,an improved YOLOv5s smoke and flame detection method is proposed.Firstly,in order to solve the problem that the Neck feature fusion of smoke and flame is not accurate and the effect is poor,a new channel attention mechanism,Scoring module,is proposed to score features of each channel.Features with high scores are selected for feature fusion and features with low scores are filtered to avoid introducing too many redundant features.On the premise of not increasing too much computation burden,the module can enhance feature fusion ability and detection accuracy.Then,in order to improve the prediction ability of the Head layer,α-EIOU is used to replace GIOU as the prediction box regression loss to improve the prediction accuracy of the prediction box.Finally,the improved Mosaic data enhancement method is used to solve the problem of small data set and single data form,expand the sample data,and improve the generalization ability of the model.As a result,the mean average precision of the modified YOLOv5s model is improved by 4.7%,while the detection speed reaches 212 frames per second.Meanwhile,it performs well in the comparison experiment with other improved YOLOv5s.It achieves good detection effect in the image with complex environment,and can meet the task of smoke and flame detection in complex environment.