This is about image classification by Deep Convolution Neural Network. The designed deep neural network is trained on 1800 images of six different kinds of typical surface defects of 200 × 200 pixel resolutions. The image datasets are obtained from NEU surface defect database.

1. Metal Surface Defect Inspection
Through Deep Neural Network

2. Goal
Deep Neural
Model
Defect
OK
Cracking
Patches
Inclusion
Rolled
Pitting
Scratching
Test Image
Image Classification ProcessFigure :

3. Agenda
Metal Surface Defects
Deep Neural Network
Defects Detection Method

4. Metal Surface Defects
1

5. Types Of Defects Will Inspect
Scratching Cracking Inclusion
RolledPatchesPitting
Six Types of Defect & LabelFigure :

6. Why Metal Surface
Defect Inspection
Needed ?

7. Deep Neural Network

8. Artificial Intelligence
‘‘Artificial Intelligence involves Machine that has
Human Intellectual Properties’’
Artificial Intelligence
Artificial
Intelligence
Artificial Intelligence BasicFigure :

9. Machine Learning
Machine Training AlgorithmFigure :

10. Deep Learning
Deep Neural model
Deep Learning AlgorithmFigure :

11. Defect Detection Method
1

12. Dataset
NEU surface defect databaseFigure :

13. Training Dataset
Images Labels
Feature
Extraction
Features
Deep Neural learning Algorithm
Dataset Training AlgorithmFigure :

14. Training Algorithm Code
Training Algorithm CodeFigure :

15. Training Algorithm Code
Training Algorithm CodeFigure :

16. Training Algorithm Code
Training Algorithm CodeFigure :

17. Testing Dataset
New Images
Labels
Training Model
Dataset Testing AlgorithmFigure :

18. Testing Algorithm Code
Testing Algorithm CodeFigure :

19. Testing Dataset
Testing A Sample ImageFigure :
Deep Neural
Model

20. Result
Images Label DetectedFigure :

21. Thank You