An Effective Segmentation and modified Ada Boost CNN based classification model for Fabric Fault Detection system

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 7

Year of Publication : 2020

Authors : Immadi Murali Krishna, Pendem Durga Bhavani, Tiriveedhi M S Madhuvani, Vajja Poojitha

10.14445/23488387/IJCSE-V7I7P106

How to Cite?

Immadi Murali Krishna, Pendem Durga Bhavani, Tiriveedhi M S Madhuvani, Vajja Poojitha, "An Effective Segmentation and modified Ada Boost CNN based classification model for Fabric Fault Detection system," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 7, pp. 34-40, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I7P106

Abstract:

By rapidly growing the production of fabrics in textile industry, fabric faults are most common slipup in the fabric manufacturing process. Inspection of fabrics and finding defects in the fabrics are too difficult along with the speed of production. Fabric defect detection plays a major role in the quality
control in textile industry. The major objective of our proposal is to produce the high quality fabrics in the shortest period of time using machine learning Techniques. By increasing the various data sets in the fabric fault detection, the conventional classification techniques are not able to produce the accuracy on predicting the fault with low inspection time. To improve the accuracy and to predict the fabric defect within the inspection time, we propose An Effective Segmentation and modified Ada Boost CNN based classification model for Fabric Fault Detection System.

Keywords:

Ada Boost CNN, Morphological edge detection, Pre Processing.

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