Abstract
The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.
Highlights
The fault diagnosis technology and working condition monitoring technology of the pumping unit have always been the focus of the oilfield
Thedifferent purpose detectors of the detection boxinisthis to accurately find the location and size of the is to anbe algorithm to You only look once version 3 (YOLOv3) and core thinking; the comparison of target found in similar each frame and markinitperformance out
The Faster R-convolutional neural networks (CNN), Single Shot Multi-Box Detector (SSD), and YOLOv3 algorithms used in the experiments in this paper were used as detectors in the tracking framework proposed in this paper
Summary
The fault diagnosis technology and working condition monitoring technology of the pumping unit have always been the focus of the oilfield. The commonly used fault diagnosis methods are mainly manual analysis and indicator diagram diagnosis. The dependence of a large number of high-precision sensors and high-sensitive devices increases the original cost of working condition detection and gradually increases the requirements of staff [1]. The whole process takes a lot of time, and even real-time working conditions cannot be obtained. This has posed a great challenge to the detection of field working conditions of oil field pumping wells [2]. With the gradual maturity of UAV technology, more and more projects have been launched around
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