红外技术2024，Vol.46Issue(5)：522-531,10.
基于深度学习的管道热图像泄漏识别
Identification of Pipeline Thermal Image Leakage Based on Deep Learning
摘要
Abstract
To reduce the difficulty of detecting tiny leakages at multiple leakage points in liquid pipelines,it is necessary to improve the detection accuracy and speed of the leakage points.Bilateral filtering based on nonlinear stationary wavelets is proposed to achieve image noise reduction by building a water circulation pipeline leakage experiment system,changing the sizes and number of the leakage points,changing the temperature of the conveying medium,and applying an infrared thermal imager to monitor the small leakage of the single and complex leakage points.Combined with infrared nondestructive testing technology and a YOLO v4 network model,this study realized the automatic intelligent detection of single and multiple leakage points of liquid pipelines.The results show that compared with the traditional filtering algorithm,the peak signal to noise ratio and structural similarity evaluation indexes of the noise reduction method are improved.The model can quickly and accurately detect and locate single and multiple leakage points of pipelines.The average detection accuracy(mAP)values of the single and multiple leakage points in complex environment reach 0.9822 and 0.98,respectively.Further,the accuracy rates reach 98.3%and 98.36%,and the single frame detection times reach 0.3021 s and 0.3096 s,respectively.This helps realize the identification of leakage points under complex background interference.In comparison with YOLO v3,Faster R-CNN,and SSD 300,the YOLO v4 algorithm has better accuracy,mAP,and t for the detection of single and multiple leakage points and has a higher detection accuracy and detection efficiency.关键词
输液管道/红外无损检测技术/图像降噪/自动检测/MATLABKey words
liquid pipeline/infrared non-destructive testing technology/image noise reduction/automatic detection/MATLAB分类
计算机与自动化引用本文复制引用
陈秋艳,张新燕,贺敏,田义春,刘宁,郭瑞,王晓辉,游思源,张修坤..基于深度学习的管道热图像泄漏识别[J].红外技术,2024,46(5):522-531,10.基金项目
国家自然科学基金(51904170) （51904170）
山东省自然科学基金博士基金(ZR2019BEE041). （ZR2019BEE041）
