Implementation of Drill Pipe Joint Positioning Under Binocular Vision
The rapid development of intelligent robot technology has promoted the automation and intelligence of drilling equipment.In make-up and break-out of drill pipes currently,traditional tool joint positioning technique is difficult to apply and not fully automatic.To address this problem,a smart tool joint detection method based on improved YOLOv5x was proposed.Then,a tool joint positioning model was built under binocular vision.The model extracts image features of tool joints based on convolutional neural network(CNN)to implement automatic recogni-tion.It integrates the convolution block attention module(CBAM)to improve the feature extraction and expression ability.It also introduces a larger scale feature map as a small object detection layer to reduce the false detection and omissive detection of small objects in tool joint images.Combined with the semi-global block matching(SG-BM)algorithm,it implements precise positioning of tool joints under binocular vision.The performance of the im-proved algorithm was verified through network training,and a positioning test of the tool joint was conducted in a simulated experimental environment.The test results show that the improved YOLOv5x algorithm has an average ac-curacy of 98.6%,which is 3.0%higher than the original YOLOv5x algorithm.The average positioning errors of the upper and lower joints are 7.64 mm and 6.56 mm respectively,which meet the engineering error requirements and have certain engineering application value.The conclusions provide technical reference for accurate detection and precise positioning of tool joints.
万方数据
维普
