基于深度学习的红外目标成像液位检测方法

Infrared Target Imaging Liquid Level Detection Method Based on Deep Learning

容器液位检测是工业生产及化工原料储存、运输过程中的重要环节,针对现有液位检测技术中传感器布置容易受空间限制,在高温高压、灰尘、潮湿等特殊环境下传感器寿命短等问题,提出了一种基于深度学习的红外目标成像液位检测方法。通过对容器红外图像标注数据集进行优化训练,得到可以准确识别容器内液体百分比含量的模型。首先,构建储罐液位标准数据集,并搭建基于Pytorch的深度学习目标检测框架。然后,在输入端对图像进行数据增强,调整模型的宽度和深度,优化训练检测模型。最后,采用特征金字塔网络和路径聚合网络结构融合不同尺寸特征图的特征信息,用联合交并比计算边界框的回归损失,并在后处理过程中引入加权非极大值抑制。实验结果表明,该模型具有较好的鲁棒性和识别效果,在交并比为0.5时的平均精度均值可达到0.804。

The detection of container liquid level is an important link in the process of industrial production,storage and transportation of chemical raw materials.Aiming at the problems that the sensor layout in the existing liquid level detection technology is easily limited by space and the short service life of the sensor in special environments such as high temperature,high pressure,dust and humidity,a method of infrared target imaging liquid level detection based on deep learning is proposed in this paper.Through the optimization training of the infrared image annotation data set of the tank liquid level,the model that can accurately identify the percentage content of liquid in the container is obtained.First,construct a standard data set of tank liquid level and build an image detection framework based on Pytorch's deep learning.Then,enhance the data on the image at the input end,adjust the width and depth of the model,and optimize and train the detection model.Finally,the feature pyramid network and path aggregation network structure are used to fuse the feature information of different size feature maps,the complete intersection over union is used to calculate the regression loss of the bounding box,and the weighted non maximum suppression method is introduced in the post-processing process.The experimental results show that the model has good robustness and recognition effect,the mean average precision is up to 0.804 when intersection over union is 0.5.