RESEARCH ON THE DAMAGE RECOGNIZING METHOD OF IMPERVIOUS LAYER OF LANDFILL BASED ON MACHINE VISION

CHEN Ya-yu; LI Jian-long; SUN Ji-sheng; WANG Hong-da; BI Shi-jun

doi:10.13205/j.hjgc.202108019

Volume 39 Issue 8

Jan. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2021 > 39(8): 136-140,149

CHEN Ya-yu, LI Jian-long, SUN Ji-sheng, WANG Hong-da, BI Shi-jun. RESEARCH ON THE DAMAGE RECOGNIZING METHOD OF IMPERVIOUS LAYER OF LANDFILL BASED ON MACHINE VISION[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(8): 136-140,149. doi: 10.13205/j.hjgc.202108019

Citation:

CHEN Ya-yu, LI Jian-long, SUN Ji-sheng, WANG Hong-da, BI Shi-jun. RESEARCH ON THE DAMAGE RECOGNIZING METHOD OF IMPERVIOUS LAYER OF LANDFILL BASED ON MACHINE VISION[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(8): 136-140,149. doi: 10.13205/j.hjgc.202108019

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RESEARCH ON THE DAMAGE RECOGNIZING METHOD OF IMPERVIOUS LAYER OF LANDFILL BASED ON MACHINE VISION

doi: 10.13205/j.hjgc.202108019

1. College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China;
2. Hebei Weicube Technology Co., Ltd, Tangshan 063000, China

Received Date: 2020-11-14
Available Online: 2022-01-18

Abstract

Abstract

The high-density polyethylene (HDPE) film of the anti-seepage layer of the landfill is easily damaged during operation. The online monitoring technology is used to determine the leakage area. After the medium on the membrane removed, the loopholes need to be accurately identified to provide a visual basis for welding process. Therefore, a machine vision-based damage identification method for impermeable layer in landfill was proposed. First, perform image processing on the sample set, including image grayscale, Gaussian filter denoising, point operation enhancement, threshold segmentation, and mathematical morphology processing. Secondly, the number of connected domains, damage area, circumference, major axis, minor axis and axial ratio were extracted according to the morphological features of the image. The retention method weas used to divide the sample set into a training set and a test set, and then the extracted features were used as the input for training SVM. Finally, multiple SVMs were used for classification and recognition. Experiments showed that the overall recognition accuracy of the classifier was 98.33%, among which the accuracy of block damage recognition was 98.24%, and the stitch damage was 98.42%.
- landfill impervious layer,
- machine vision,
- image processing,
- SVM,
- damage identification

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References(20)

References

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