Summary Rod pump systems are the primary production tools in oilfield development. Analyzing their working conditions provides a theoretical foundation for formulating production optimization plans and adjusting equipment parameters. Existing machine learning–based condition analysis methods rely on dynamometer cards and cannot capture the actual operational trajectory of the pumping unit. To address this issue, this paper proposes a keypoint detection method for pumping units based on pose estimation of heatmap-free joint detection from video data. A data annotation scheme suitable for the task of detecting pumping unit keypoints was developed, and the learning criteria for this task were optimized. An end-to-end heatmap-free pose estimation algorithm was used to process images of the pumping unit, yielding predicted keypoint positions, thereby enabling the identification of the keypoint motion trajectories of the pumping unit. Experiments validated the proposed method and compared it with general learning criteria. Results show that this method accurately captures the keypoint positions of the pumping unit, with the optimized learning criteria model improving by more than 5% compared with general methods and increasing the keypoint object keypoint similarity (OKS) by more than 30%. The model’s results can be used for the actual operational trajectory recognition of the pumping unit, automatically calculating the motion parameters of the polished rod, and intelligently assessing the balance and working condition analysis of the pumping unit. This realizes the intelligent application of video surveillance data, significantly contributing to the dynamic study of rod pump systems.

Trajectory Recognition and Working Condition Analysis of Rod Pumping Systems Based on Pose Estimation Method with Heatmap-Free Joint Detection