Investigation of Corrosion Intensity on Metal Structures Using Image Processing Algorithms

Hosallimath, Virupaxayya V.; Awati, Anupama; Patil, Meenakshi

doi:10.1109/c2i456876.2022.10051605

Cited by 1 publication

(1 citation statement)

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“…After extraction of metal part by color space conversion method, the rusted part is extracted from the metal part. In order to better illustrate the effectiveness of this method in extracting the rust, several methods have been introduced as comparative methods (DA Net [21], 2021; Grayscale [33], 2022), shown in Table 12. The data source comes from Figure 12d.…”

Section: Extraction and Assessment Of The Rustmentioning

confidence: 99%

A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value

Hu,

Duan,

Zhao

et al. 2023

Sensors

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Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved.

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Section: Extraction and Assessment Of The Rustmentioning

confidence: 99%