Marine-Hydraulic-Oil-Particle Contaminant Identification Study Based on OpenCV

Wang, Chenyong; Yang, Chao; Zhang, Hongpeng; Wang, Shengzhao; Yang, Zhaoxu; Fu, Jingguo; Sun, Yuqing

doi:10.3390/jmse10111789

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Compared to conventional techniques, the utilization of this approach yields a substantial enhancement in the precision of extracting morphological characteristics and augments the dependability of wear particle pattern identification. Wang et al introduced a sensor for detecting oil wear particles using image recognition processing, which is based on the OpenCV framework in 2022 [91]. The particle images in the hydraulic oil were preprocessed, and the contours of particle pollutants were extracted using the Canny operator edge detection algorithm.…”

Section: Image Processing Sensormentioning

confidence: 99%

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Han,

Mu,

Liu

et al. 2023

JMSE

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In the field of marine engineering, the friction and wear experienced by rotating mechanisms are recognized as significant contributors to the failure of marine machinery. In order to enhance the safety and dependability of marine ship operations, the implementation of on-line oil wear debris particle detection sensors enables the on-line monitoring of oil and facilitates the rapid identification of abnormal wear locations. This paper provides a critical review of the recent research progress and development trends in the field of sensors for on-line detection of oil wear debris particles. According to the method of sensor detection, wear debris particle detection sensors can be classified into two distinct categories: electrical and non-electrical sensors. Electrical sensors encompass a range of types, including inductive, capacitive, and resistive sensors. Non-electrical sensors encompass a range of technologies, such as image processing sensors, optical sensors, and ultrasonic sensors. Finally, this review addresses the future research directions for wear debris particle detection sensors in light of the challenging problems currently faced by these sensors.

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Section: Image Processing Sensormentioning

confidence: 99%

A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors

Han,

Mu,

Liu

et al. 2023

JMSE

Self Cite

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“…In the field of computer vision, image processing techniques and object detection algorithms represent two crucial branches of technology that have found extensive applications. Wang et al [31] employed traditional image processing techniques, such as the Canny operator and Douglas-Peucker algorithm to accomplish shape detection and attribute recognition of contaminants within maritime hydraulic oil. Huang et al [32] applied recursive filtering and MSRCR image enhancement algorithms for preprocessing foreign object images in coal mining transportation.…”

Section: Related Studiesmentioning

confidence: 99%

Real-Time Detection of Slug Flow in Subsea Pipelines by Embedding a Yolo Object Detection Algorithm into Jetson Nano

Qiao,

Guo,

Huang

et al. 2023

JMSE

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In the multiple-phase pipelines in terms of the subsea oil and gas industry, the occurrence of slug flow would cause damage to the pipelines and related equipment. Therefore, it is very necessary to develop a real-time and high-precision slug flow identification technology. In this study, the Yolo object detection algorithm and embedded deployment are applied initially to slug flow identification. The annotated slug flow images are used to train seven models in Yolov5 and Yolov3. The high-precision detection of the gas slug and dense bubbles in the slug flow image in the vertical pipe is realized, and the issue that the gas slug cannot be fully detected due to being blocked by dense bubbles is solved. After model performance analysis, Yolov5n is verified to have the strongest comprehensive detection performance, during which, mAP0.5 is 93.5%, mAP0.5:0.95 is 65.1%, and comprehensive mAP (cmAP) is 67.94%; meanwhile, the volume of parameters and Flops are only 1,761,871 and 4.1 G. Then, the applicability of Yolov5n under different environmental conditions, such as different brightness and adding random obstructions, is analyzed. Finally, the trained Yolov5n is deployed to the Jetson Nano embedded device (NVIDIA, Santa Clara, CA, USA), and TensorRT is used to accelerate the inference process of the model. The inference speed of the slug flow image is about five times of the original, and the FPS has increased from 16.7 to 83.3.

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