Optimization of single filter network on visual corrosion defect

Author

Idris, Syahril Anuar ; Jafar, Fairul Azni ; Blar, Noraidah

Author_Institution

Fac. of Manuf. Eng., Univ. Teknikal Malaysia Melaka Melaka, Durian Tunggal, Malaysia

fYear

2014

Firstpage

658

Lastpage

662

Abstract

Due to the challenging environmental conditions and characteristics, the complexity of the corrosion inspection operation increases. By using software image filter to enhance image data, the object recognition technique will be able to analyze the image data accurately. A selected software filter, wavelet de-noising has been identified to enhance image data for visual corrosion inspection application. Therefore, in order to obtain a better image enhancement, neural network is used for validation. The experiment result shows neural network wavelet de-noising filter used to enhance image in order to obtain better image for visual corrosion inspection is achievable, and gives desirable result in terms of Mean Square Error and Peak Signal to Noise Ratio. This project is focusing on corrosion inspection using image.

Keywords

corrosion; image denoising; image enhancement; inspection; mean square error methods; neural nets; object recognition; optimisation; production engineering computing; wavelet transforms; image enhancement; mean square error; neural network; object recognition technique; optimization; peak signal-to-noise ratio; single filter network; software image filter; visual corrosion defect; visual corrosion inspection operation; wavelet denoising; Corrosion; Inspection; Noise reduction; Optimization; PSNR; Visualization; Wavelet transforms; Neural Network; Software Filter Image; Wavelet De-noising;

fLanguage

English

Publisher

ieee

Conference_Titel

Ubiquitous Robots and Ambient Intelligence (URAI), 2014 11th International Conference on

Type

conf

DOI

10.1109/URAI.2014.7057489

Filename

7057489