基于YOLOv5的油气井套管接箍检测方法

YOLOv5-based Detection Method for Oil and Gas Well Casing Joints

针对目前在可视化测井领域,利用人工对测井视频中的接箍进行识别校深时存在的检测效率低下、检测误差高的问题,提出一种基于YOLOv5算法的接箍智能检测方法。首先对井下的接箍图像进行采集,在缺乏公开数据集的情况下,通过数据增强的方法制作接箍数据集,再对增强后的数据集使用labelimg工具进行数据标注,然后将标注好的数据集送入YOLOv5网络中进行训练,最后使用训练好的最佳权重进行接箍识别并计数。测试结果表明,该方法能实现对当前测井视频中接箍的准确识别,接箍计数的正确率为100%,并且检测效率高,平均每帧检测时间为15 ms。

At present,in the field of visual logging,the manual identification and depth calibration of casing joints from logging videos results in low detection efficiency and high detection error.To address this issue,a intelligent casing joint detection method based on YOLOv5 algorithm is proposed.Firstly,the image of the downhole casing joint is collected.In the absence of a publicly available dataset,a casing joint dataset is created through data enhancement method,and the enhanced dataset is labeled using the labelimg tool.Then,the labeled dataset is fed into the YOLOv5 network for training.Finally,the casing joints are recognized and counted using the trained optimal weights.The test results show that this method can accurately identify the casing joints in the current logging video,with a 100% accuracy in counting of the casing joints,and there is high detection efficiency,with an average detection time of 15 ms per frame.