基于EACN模型的高精度测温误差修正算法

Error Correction Algorithm of High Precision Temperature Measurement Based on EACN Model

针对红外热像仪测温精度不足以及速度较慢的问题,提出了一种融合通道注意力机制的温度修正模型EACN(Efficient Attention Compression Networks Module)。该模型首先通过1×1卷积实现特征降维压缩,以此减少模型参数。其次引入通道注意力机制ECA,在特征映射模块阶段增强通道间特征显著性表达,以此弥补降维压缩所损失的特征信息,且进一步提高模型特征表征能力。最后,通过跳跃连接,在特征重建阶段结合浅层特征信息与语义空间信息,从而提高温度修正精度。本实验采用两种数据策略在自建数据集上进行实验。实验结果表明,与SRCNN和VDSR模型相比,EACN模型无论在修正精度方面,还是速度方面表现均最优。

A temperature correction model,EACN,based on a channel attention mechanism is proposed to address the issues of insufficient accuracy and slow speed in temperature measurements from thermal imaging cameras.First,the model parameters are reduced by decreasing the features through 1x1 convolution.Second,we introduce a channel attention mechanism,ECA,to enhance the feature saliency expression between channels in the feature mapping module stage,compensating for lost feature information during dimensionality reduction and compression,thereby further improving the feature characterization capability of the model.Finally,through skip connections,shallow feature information is combined with semantic space information in the feature reconstruction stage,thus improving temperature correction accuracy.In this experiment,two data strategies were used on a self-built dataset.The experimental results show that the EACN model outperforms the SRCNN and VDSR models in both correction accuracy and speed.