Memory Enhanced Dynamic Conditional Random Fields Embedded Pairwise Potential CNN for Fabric Defects Identification
How to Cite?
B. Vinothini, S. Sheeja, "Memory Enhanced Dynamic Conditional Random Fields Embedded Pairwise Potential CNN for Fabric Defects Identification," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 227-234, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P229
In the textile industry, defect identification is the most crucial process for localizing Fabric Defects (FDs) and enhancing yarn quality. In earlier centuries, many techniques were discussed to identify the FDs automatically. Among those, a hybrid technique called Pairwise-Potential Activation Layer in Convolutional Neural Network (PPAL-CNN) localizes the fine structures in textile imagery through integrating dynamic AL on CNN and a PP factor in the Conditional Random Fields (CRFs). However, this CRF should be supplied a priori rather than learned. It was hard for a complex interaction between FDlabels/classes while performing multiple, or long-range dependencies exist. Therefore, this paper proposes an Enhanced PPAL-CNN (EPPAL-CNN) technique to manage the complex structure interaction of FDs. First, the CRF is extended by integrating external memory strategies stimulated from the memory networks and thus facilitating CRFs for interpretation beyond localized characteristics and have access to the complete image. It encompasses the memory and Dynamic CRF (DCRF) layers. The memory layer is partitioned into input, output, and current input memory. The interpretations of input and output memory have interacted through an attention model in which weights are calculated by the relation of an input and a current input memory. Then, an outcome of the memory layer is taken as input to the DCRF layer. The DCRFs are simplified linear CRFs and are used for defining the shared hidden state and complicated relation between labels. Its factorial construction includes the relations among cotemporally labels, explicitly modeling constrained likelihood dependencies among various labels. So, a high-level Markov dependency among labels is modeled by considering the external memory. Finally, the investigational outcomes exhibit that the EPPAL-CNN achieves 93.36% accuracy compared to the PPAL-CNN technique using the TILDA database.
Fabric defects, Defect identification, CNN, Pairwise-potential activation, CRFs, Memory network.
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