Abstract In currently hot rolling heavy rail surface faults detecting, speed is slow and its precision is low.So a suit of surface defect detection system for hot rolling heavy rail based on the machine vision is produced. Too dark and sun regional overlapping fusion method and image correlation between pixel lines algorithm is analysised,and a fuzzy spiking neural network used to make a classification for the characteristics of low SVM training algorithm is researched.Using above key machine vision technology for detection of hot heavy rail surface defects identification, the speed and accuracy of online testing can be greatly improved, and the detection correction rate is over than 90%.
XIE Zhi-jiang,XIE Chang-gui. Study on the Key Technology of Hot Rolling Heavy Rail Surface Faults of Online Detecting and Recognition[J]. Acta Metrologica Sinica, 2014, 35(2): 139-142.
2014, Vol. 35 
