Application of YOLO V4 Model in Flame and Smoke Detection of Sour Well Station
Aiming at the H2S and other corrosive substances sulfur-containing natural gas can easily lead to leakage of well sta-tion equipment and pipelines,which can easily lead to fire.However,the commonly used flame and smoke detection instruments and algorithms are easily affected by the complex environment of well station,and there are certain risks in manual inspection of sour well station.It proposes a flame and smoke detection method for sour well station based on deep learning target detection model.First of all,we train the open flame and smoke data set with the YOLO V4 target detection model which can detect real-time in the mobile terminal;then,we train the well station flame and smoke data set with the migration learning method to ex-tract the well station flame and smoke features;finally,after the transfer learning the average accuracy of.YOLO V4 target de-tection model is 99.62%.YOLO V4 target detection model cooperating with the inspection robot will have better fire warning and rescue reconnaissance ability for the sulfur-containing well station.
Fire DetectionSmoke DetectionDeep LearningTarget DetectionYolo V4Transfer Learning
NETL
NSTL
万方数据
维普
