Automatic Identification Method of Collar Based on Faster-RCNN Network
Depth is very important for determining the location of casing defects and perforation in visual logging.Howev-er,existing sounding systems have certain depth errors.There are some problems such as time consuming and slow detection speed when searching the collar manually by video.This paper firstly enhances the downhole video collected by VideoLog visual logging technology.The Faster-RCNN model is introduced and ResNet50 network is used as feature extraction network to extract collar features.Finally,the identification and positioning of the collar are completed through the interest area pooling network and the full connection layer.The model has an average accuracy of 0.99.In the experiment,all the connections in the video can be accurately identified,which has the advantages of fast recognition speed and high accuracy.
万方数据
维普
