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Semantic segmentation of ferrography images for automatic wear particle analysis.
- Source :
-
Engineering Failure Analysis . Apr2021, Vol. 122, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • An improved DCNN model for semantic segmentation of wear particle images. • An end-to-end process for the wear particle classification. • An Encoder-ASPP-Decoder architecture for semantic segmentation DCNN. Automatic wear particle detection and classification has remained a high priority research area for wear condition monitoring and failure analysis. In this study, a deep convolutional neural network (DCNN) with three modules, namely, an encoder, atrous spatial pyramid pooling (ASPP), and a decoder, is constructed. Instead of using handcrafted features, the DCNN can automatically learn features through a layer-wise representation and realize semantic segmentation, i.e., segmentation and identification concurrently, of five types of wear particles in ferrograph images using end-to-end processing. Experimental results show that the DCNN achieves 82.5% accuracy. This proposed method unifies the segmentation, classification, and edge location of the wear particles into a single model, avoids the accumulation and transmission of errors caused by numerous steps applied in a traditional linear process, and improves the efficiency and accuracy of the wear particle analysis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13506307
- Volume :
- 122
- Database :
- Academic Search Index
- Journal :
- Engineering Failure Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 149221169
- Full Text :
- https://doi.org/10.1016/j.engfailanal.2021.105268