Optoelectronic leak detection system for monitoring subsea structures

Moodie, D.G.; Costello, Lori; McStay, Daniel

doi:10.1117/12.854483

Cited by 1 publication

(2 citation statements)

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“…To address the above problems, the critical working regions, such as offshore platforms and vessels, should be monitored for subsea gas leaks so that related operators can raise the corresponding alarm at an early stage and thereby make timely emergency and prevention measures. Past decades have witnessed the development of subsea gas leak monitoring approaches and technologies [13][14][15][16][17][18][19][20][21][22][23]. These works can be classified into two categories: software-based internal methods and hardware-based external methods [14,22].…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the internal method mainly monitors related gas flow parameters, such as flow mass or volume, negative pressure waves, and gas flow rate, and then judges whether a subsea gas leak is occurring [14,19,22]. By contrast, the external method needs external sensors such as hydrophones [13,19], optoelectronic sensors [17,18], fiber-optic cables [15,16,23], and optical cameras [14,20,21,24] to realize the subsea gas leak monitoring. Compared to the external method, the internal methods have some drawbacks [14,19].…”

Section: Introductionmentioning

confidence: 99%

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Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches

Zhu

Xie

et al. 2023

Sensors

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Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of monitoring underwater gas leakage, but the shortcomings of huge labor costs and severe false alarms exist due to related operators’ operation and judgment. This study aimed to develop an advanced computer vision-based monitoring approach to achieve automatic and real-time monitoring of underwater gas leaks. A comparison analysis between the Faster Region Convolutional Neural Network (Faster R-CNN) and You Only Look Once version 4 (YOLOv4) was conducted. The results demonstrated that the Faster R-CNN model, developed with an image size of 1280 × 720 and no noise, was optimal for the automatic and real-time monitoring of underwater gas leakage. This optimal model could accurately classify small and large-shape leakage gas plumes from real-world datasets, and locate the area of these underwater gas plumes.

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