In order to realize the automatic detection of weld surface defects

a weld surface quality detection method based on line laser vision sensor is studied

the extraction algorithm of weld surface defect characteristics and a classification model of weld defects are analyzed. For the defects such as pit

undercut and blowhole in the weld surface

the geometric shape and spatial distribution characteristics of different defects in laser stripe images are analyzed. Also

the feature points of weld defects are extracted by slope intercept method and segmented interval detection method. Seven characteristic parameters of weld defects are detected by feature extraction method to design a weld defect classification model based on three-layer BP neural network. These extracted seven defect characteristics are input for network training as the network characteristics. The results show that the proposed method can detect the weld surface defects such as pit

undercut and gas pore

and the overall recognition rate of the classification model can reach 91.51%.