Weld recognition based on key point detection method
In order to ensure the quality of automatic weld-ing and improve the accuracy and adaptability of weld identi-fication,a key point detection method for weld feature extrac-tion is proposed.A weld feature extraction network was de-signed based on the convolutional neural network.The net-work extracted weld feature by convolution and pool opera-tion.The feature map from deep layer is sampled up,and then the feature map from deep layer and shallow layer are fused to improve the accuracy of weld feature extraction.The feature point position of weld seam is predicted by the thermal image of weld seam,and the recognition and location of many kinds of groove weld seam are realized,and eliminating the need for non-maximum suppression algorithm,which improves the fea-ture extraction speed.The network model is trained by collect-ing different weld feature images.The experimental results show that the root mean square error of weld feature point loca-tion is 0.187 mm.The network model designed in this re-search has high detection accuracy in the weld feature point re-cognition task,and has strong adaptability and generalization,and meets the requirements of automatic welding.
NETL
NSTL
万方数据
维普
