Parts Surface Structure Image Classification Detection System Design
Abstract: In order to
accomplish the automatic nondestructive testing, a parts surface structure
image classification detection system is designed. A series of parts surface
texture images have been obtained from different processing methods for feature
analysis and the combination of pre-processing method by MATLAB image
processing toolbox has been put forward, using statistical analysis method for
feature extraction. Based on the established BP neural network training
optimization identification system, thispaper realized the recognition of parts
surface resulted from four kinds of processing methods: turning, milling,
planning and grinding. The research results show that the deficit value of gray
level co-occurrence matrix and the histogram matrix variance value can be
regarded as characteristic parts of the surface texture structure value,
providing foundations for further development of parts surface structure
detection.
Keywords: Surface structure,
image detection, feature extraction, BP neural network, classification and recognition
Author: Min Cui
Journal Code: jptkomputergg160071