Author(s)

Yuxin Wang ¹

Affiliation(s)

¹ 2970 International Dr. APT 109C, Ypsilanti, Michigan, 48197, USA

Corresponding Author

Yuxin Wang

ABSTRACT

Oil pipe is a vital infrastructure in the process of oil exploitation and transportation, which is in the environment of high pressure, high temperature and corrosive media for a long time, prone to corrosion, cracks, wear and other failure problems, seriously affecting production efficiency and safety. Traditional methods of pipe failure detection mainly rely on manual visual inspection or physical inspection equipment, which has some problems such as low detection efficiency, poor accuracy and large manual error. In recent years, the application of deep learning technology in industrial image recognition has gradually become a research hotspot, especially the advantages of convolutional neural network (CNN) in image classification and feature extraction, which provides a new solution for the failure detection of petroleum pipes. In this paper, an intelligent recognition algorithm based on deep learning for macroscopic image of oil pipe failure is proposed. The CNN model is used to automatically classify and recognize the pipe failure image, and the generalization ability of the model is improved by data enhancement technology. The experimental results show that the proposed algorithm has high accuracy and robustness in the oil pipe failure identification task, especially in the complex environment, it can effectively reduce false detection and missing detection. Compared with traditional methods, this algorithm not only improves the accuracy of failure detection, but also has strong real-time performance and scalability. Through the visualization analysis of the model, the ability of the deep learning model to automatically learn the failure characteristics of the pipe is further verified. Future research will further optimize the model structure, improve the deployment efficiency and practicability in the industrial field, and provide strong support for the intelligent testing of oil pipes.

KEYWORDS

Deep learning, Convolutional neural networks, Oil pipes, Failure detection, Macro image recognition

CITE THIS PAPER

Yuxin Wang, Research on Intelligent Macro Image Recognition Algorithm of Oil Pipe Failure Based on Deep Learning. Journal of Image Processing Theory and Applications (2025) Vol. 8: 1-7. DOI: http://dx.doi.org/10.23977/jipta.2025.080101.

REFERENCES

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