BY S.KARDHEEPAN(200902036)

S.DIVYA(200902018) E.ASWINI(200902130) IV year ECE Rajalakshmi Engineering College, Thandalam.

UNDER THE GUIDANCE OF Mr.T.Manikandan M.E., (Ph.D.) Associate Professor Department of ECE

Rajalakshmi Engineering College, Thandalam.

oThe detection of features and classification based on it in a digital image is the key requirement in quality control systems in production and process.

o The inspection of surface texture is an important part of many industrial quality control applications.

o An inspection system to replace human inspectors should be capable of detecting flaws such as scratches, stains, (textural defects) and dents, cracks, blow holes (structural defects) occurring in various shapes and sizes.

oMachine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry.

oA Machine Vision System can be offline or online.

oThe primary uses for machine vision are automatic inspection and industrial robot guidance .

oCommon MV applications include quality assurance, sorting, material handling, robot guidance, and optical gauging.

oThis project aims at detecting surface crack defects on an disc metal surface.

oDefect detection using texture analysis.

oAbout 92% efficiency will be achieved.

Input Relay image

Image splitting

Bottom-hat filtering

Image fusion

Adaptive thresholding

Connected component labelling

Crack detection

Fig :- Proposed Method II

AlgorithmStep 1: Read the grayscale(input) image.

Step 2: Input images is splitted into sub-images.

Step 3:Then applying bottom-hat filtering to each sub-images.

Step 4: After that all the sub images are fused together and then adaptive threshold is applied.

Step 5:Then connected component labelling is done.

Step 6: Find the area which is greater than the particular value is removed as long as the crack is detected. Finally visualize the result.

I/p image

Segmented Image

Bot-hat Transformation-Segmented Image

Fused Image

Adaptive Threshold

Labelled and Noise Removed

ConclusionConclusion

• The input grayscale images were divided into sub-images and then bottom-hat filtering was applied.

• Sub images were then combined together to apply the adaptive threshold and then the images were labeled .

• After that the area which were greater than the particular value was removed to obtain the crack.

Works yet to be completed:

• Recapturing the image using more resolution camera

• Applying superior pre-processing techniques to improve the output results

References1.Ali M. Albishi , Muhammed S. Boybay, and Omar M. Ramahi, “Complementary Split-Ring Resonator for Crack Detection in Metallic Surfaces”,In : IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, VOL. 22, NO. 6, JUNE 2012

2.Anders Landström and Matthew J. Thurley “Morphology-Based Crack Detection for Steel Slabs”, In : IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 6, NO. 7, NOVEMBER 2012

3.Andrew McClanahan, Sergey Kharkovsky, Andrew R. Maxon, Reza Zoughi and Donald D. Palmer “Depth Evaluation of Shallow Surface Cracks in Metals Using Rectangular Waveguides at Millimeter-Wave Frequencies” In : IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 59, NO. 6, JUNE 2010

4.Jie Zhao, Y ongmin Yang and Ge Li, “The Cold Rolling Strip Surface Defect On-Line Inspection System Based on Machine Vision”, In : 2010 Second Pacific-Asia Conference on Circuits, Communications and System (PACCS)

5.Limin Chen, Yin Liang, Kaimin Wang,“Inspection of Rail Surface Defect Based on Machine Vision System” In : This work was supported by Jiangxi Provincial Department of Education Project GJJ09128(2009).

6.Roman Louban “Image Processing of Edge and Surface Defects” Springer Series in materials science 123