Research and Optimization of Tubular Column Joint Identification Technology for Intelligent Drilling Platforms

Abstract

 An improved target detection algorithm based on YOLOv5s is proposed to be applied to identify and localize tubular column joints in The improved SC-YOLOv5s model is obtained by combining SPPF, CBAM and the improved multi-scale feature fusion network BiFPN. The improved SC-YOLOv5s model is obtained by combining SPPF, CBAM and the improved multi-scale feature fusion network BiFPN. The SPPF structure introduces spatial pyramid pooling in the network, which can effectively capture the features of the target at different scales and improve detection precision. different scales and improve the detection precision and recall of the target, while the CBAM attention mechanism can adaptively learn the target's spatial and channel feature relationships. s spatial and channel feature relationships, improving the model's ability to distinguish between targets and anti-interference ability The improved multi-scale feature fusion network BiFPN combines Bottom-up and Top-down feature transfer mechanisms, which can better fuse feature information at different levels, making the model's ability to distinguish between targets and anti-interference ability. information at different levels, making the model better adapted to detection tasks in various complex environments. The experimental results show that the improved algorithm achieves the following results that the improved algorithm achieves significant performance improvement in the pipe-column joint detection task, with a mAP value of 99.44%, a frame rate of 136 FPS, and an accuracy of 98.4%, which is 6.6% higher than the original model. The algorithm also improves sensing capability, accuracy and robustness, and outperforms the original model. The algorithm also improves sensing capability, accuracy and robustness, and outperforms other mainstream models in terms of precision. This provides a reference for automatic processing of tubular columns in intelligent oil rigs.