GUO Lianjin1, LUO Bingjun2

 

(1.Department of Electrical and Mechanical Engineering, Dongguan Polytechnic, Dongguan 523808, China;

 

2.GreatSense Automatic Instrument Co., Ltd., Guangzhou 510660, China)

 

 

Abstract: Aiming at the low SNR and feature complex of the steel surface defect images, which leading to the existing steel surface defect pattern recognition and classification method has poor realtime, low precision, and poor adaptability, classifier based on artificial neural network was studied, to achieve the classification of the steel surface defect. According to the characteristics of surface scratch, corrosion, pitting, inclusions and roller printing, the five typical defects on steel plate surface, geometric features, grayscale characteristics and Hu moment feature were extracted from defect image signal.Comprehensive characterizations of defect feature information of the 13d feature vector were selected as input of neural network, the basis for defects recognition and classification was provided. To classify the surface defects of steel plate, probabilistic neural network PNN and BP neural network classifier were constructed respectively, and the test results were compared and analyzed. The resules indicate that PNN and BP neural network recognition rate were 87% and 81% respectively. It shows that PNN is better than that of BP neural network in comprehensive performance of recognition accuracy, training speed and the ability of increasing samples.

 

Key words: PNN; BP neural network; steel plate surface; defects classification