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A corrosion detection algorithm via the random forest model
Author(s): Tingting Liu; Kai Kang; Fen Zhang; Jialiang Ni; Tianyun Wang
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Paper Abstract

Corrosion is a serious issue causing damage in steel facilities. Timely inspection and repair is essential to avoid unprecedented structural failures. Employing non-destructive methods of manual inspection for large number of antennas to detect corrosion and related damages is time consuming and expensive. In addition to this, safety of inspector to climb structures possibly weakened by corrosion. In such a situation non-contact approach of automated visual inspection for corrosion and related damage detection through image processing of aerial based images is a viable option. For robust corrosion segmentation and detection, we investigate color classification based on random forest. A random forest is a statistical framework with a very high generalization accuracy and quick training times. We evaluate random forest based corrosion detection and compare it to Bayesian network, Multilayer Perceptron, SVM, Naive Bayes and RBF network. Results on a database of real images with manually annotated pixel-level ground truth show that with the IHLS colour space, the random forest approach outperforms other approaches.

Paper Details

Date Published: 14 February 2019
PDF: 6 pages
Proc. SPIE 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018), 110480B (14 February 2019); doi: 10.1117/12.2518313
Show Author Affiliations
Tingting Liu, China Satellite Maritime Tracking and Control Dept. (China)
Kai Kang, China Satellite Maritime Tracking and Control Dept. (China)
Fen Zhang, China Satellite Maritime Tracking and Control Dept. (China)
Jialiang Ni, China Satellite Maritime Tracking and Control Dept. (China)
Tianyun Wang, China Satellite Maritime Tracking and Control Dept. (China)


Published in SPIE Proceedings Vol. 11048:
17th International Conference on Optical Communications and Networks (ICOCN2018)
Zhaohui Li, Editor(s)

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