石油钻采工艺2026，Vol.48Issue(2)：139-147,176,10.DOI:10.13639/j.odpt.202509027
基于SDA-YOLO和双目视觉的钻杆识别定位方法
Drill pipe recognition and localization method based on SDA-YOLO and stereo vision
摘要
Abstract
In the context of complex drilling site environments,where the drilling rod targets exhibit cross-scale characteristics that a re difficult to perceive accurately,thus limiting the stable operation of automated drilling rigs,a method for drill pipe recognition and localization based on SDA-YOLO and stereo vision is proposed.Building on the lightweight YOLO v8n-seg model,a multi-level feature fusion module(SDI)and an attention-based scale sequence fusion module(ASF-YOLO)are introduced,and an Inner-CIOU loss function is designed to strengthen the constraints on small-scale targets.The SDA-YOLO model is constructed.To validate the effectiveness of the method,a dataset consisting of 1 600 real-field drill pipe images is created,which is divided into training and testing sets at a ratio of 8∶2.Experimental results show that the model achieves precision,recall,and mean average precision values of 99.7%,93.5%,and 98.0%,respectively,which represent improvements of 13.2,9.2,and 12.3 percentage points compared to the baseline YOLO v8n-seg model,significantly enhancing the recognition and segmentation accuracy of target drill pipes.Moreover,based on stereo vision theory,integrated with dual calibration and stereo rectification,the RAFT-Stereo stereo matching algorithm is introduced to perform dense disparity estimation,enabling high-precision three-dimensional localization of the drill pipe's inner diameter center,with an average absolute error of 7.82 mm.The study demonstrates that the proposed method can provide reliable visual perception support for automatic centering of drilling rig,intelligent make-up and break-out,and collaborative handling,thus promoting the automation and intelligence level of drilling rig operations.关键词
钻杆识别/三维定位/钻井智能化/实例分割/双目视觉/SDA-YOLOKey words
drill pipe recognition/three-dimensional localization/intelligent drilling/instance segmentation/stereo vision/SDA-YOLO分类
能源科技引用本文复制引用
王丹丹,张来斌,储胜利,郑文培,甘亦凡..基于SDA-YOLO和双目视觉的钻杆识别定位方法[J].石油钻采工艺,2026,48(2):139-147,176,10.基金项目
中国石油天然气集团有限公司科技项目"复杂油气钻采重大风险演化机理与安全智能运维方法"子课题"油气钻采安全风险机理与防控关键技术研究"(编号:2023DJ6508) （编号:2023DJ6508）
国家重点研发计划项目"陆上超深油气井井喷防控关键技术装备及示范应用"(编号:2023YFC3009200) （编号:2023YFC3009200）
教育部万米深地钻探学科突破先导项目(编号:JYB2025XDXM311). （编号:JYB2025XDXM311）
