融合结构重参数化变换的气体泄漏红外检测  

Infrared Detection of Gas Leaks Incorporating Structural Reparametric Transformations

作　　者：庄宏 张印辉[1] 何自芬[1] 曹辉柱 ZHUANG Hong;ZHANG Yinhui;HE Zifen;CAO Huizhu(Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650500

出　　处：《光子学报》2024年第1期187-201,共15页Acta Photonica Sinica

基　　金：国家自然科学基金(Nos.62061022,62171206)。

摘　　要：针对常规工业气体泄漏检测装置需泄漏扩散到一定范围并与传感器接触时才能响应的不足,提出一种融合结构重参数化变换的红外非接触式检测网络模型GRNet。GRNet模型采用Mosaic-Gamma变换的预处理方法增加泄漏样本数量并提高图像对比度以增强模型的鲁棒性;通过K-means聚类分析出适用于气体泄漏红外检测的候选框以预置模型参数;优化定位损失函数以提高模型对泄漏区域的定位准确性;采用改进后的轻量化网络RepVGG模块重构特征提取网络增强模型的特征提取能力,以实现轻量化并提高检测精度。实验结果表明,GRNet模型对氨气泄漏的平均检测精度达到94.90%,单张图像平均检测时间达到3.40 ms。采用伪色彩映射实现泄漏浓度的视觉感知效果,采用PyQt5将GRNet模型进行封装实现气体泄漏红外检测系统界面的可视化并在Jetson Nano B01嵌入式实验平台部署该模型,验证了实际工程应用的可行性和有效性,为开发气体泄漏非接触探测装置以保障涉气企业的安全生产和稳定运行提供一种有效的检测算法。Ammonia gas used in industry is colorless,flammable and explosive,and its diffusion is susceptible to interference caused by wind conditions and other meteorological factors.Traditional methods of detecting target locations need to spread the leak to a certain range and contact the sensor to respond,resulting in great safety risks for inspection workers and the environment.Therefore,it is of great significance to find a large-area,efficient,non-contact gas leakage detection method that is in line with the development trend of the times,can effectively solve the potential safety hazards of personnel,and reduce the impact of gas leakage on the environment.This paper proposes a fusion of structure-heavy parametric transformation of the infrared non-contact detection model network model GRNet.The candidate bounding box suitable for infrared detection of gas leakage are analyzed by K-means clustering to preset the model parameters.Whereafter,the gas leak infrared image is preprocessed using the data enhancement method of Mosaic-Gamma transformation,so that the image combines the contextual information of 4 different forms of gas leak areas,enriches the leak scene,and increases the training batch size in disguise during training.This improves the generalization ability of the model and improves detection accuracy.Moreover,the CIoU localization loss function is used to optimize the size and center position of the leakage area to improve the predicted accuracy in locating the leakage area.Finally,the improved lightweight RepVGG module is adopted to reconstruct the feature extraction network instead of the convolutional layer of the feature extraction network,which enhances the feature extraction capability of the model,reduces the number of model parameters,compresses the size of the model,and improves the speed of model inference.The final GRNet model for ammonia leak infrared detection improves the mean average precision,single image test time,model size,and number of parameters by 2.70%,11.76%,27.43%,and 28.90%over th

关 键 词：目标检测 气体泄漏检测 定位损失函数 图像预处理 聚类分析 结构重参数化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]