In modern textile industrial processes,textile defect recognition is a very important task for quality control of this vital industry. As deep learning has revolutionized the machine learning domain from classification to regression.This project will evalaute the potentialso of critical machine-lear
Textile defect images defection through deep learning approach
In modern textile industrial processes,textile defect recognition is a very important task for quality control of this vital industry. As deep learning has revolutionized the machine learning domain from classification to regression.This project will evalaute the potentialso of critical machine-learning method, the deep convolutional neural network (CNN) , for recognition of the textile defect images.
to do the singel label classification of fabric defect images using deep convolutional neural network and comparing its results with other deep learning methods.
To validate the proposed methodology
Tools:
Python, Image preprocessing And Convolutional Neural Network, Tensorflow and Keras
Methodology:
Import libraries
Collecting and loading Textile defect images
Preprocessing
Building CNN
Training the CNN
Predicting
Evalaute
This project will be able to classsify fabric defect using deep learning reducing the error rate in human inspection which is 40% to 60%.
Less defect textile results in more economic and profit growth as the indsutry faces much of the profit loss due to poor or defected quality of fabric.
High Processing Power
Increased Ram for training the model and preproccesing of the images
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Google Colab Pro+ | Equipment | 1 | 10000 | 10000 |
| 2 GB Grahpic card | Equipment | 1 | 22000 | 22000 |
| Keyboard | Miscellaneous | 2 | 3000 | 6000 |
| Mouse | Miscellaneous | 2 | 2000 | 4000 |
| Total in (Rs) | 42000 |
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