Laser stripe extraction of weld seam based on U-Net covolutional neural network
Aiming at the interference problems such as strong dazzling light,strong arc light and splash covering in laser fringe images of steel welds,a weld laser fringe feature extraction method based on a U-Net convolutional neural network is proposed to fuse the details of the underlying convolution with the abstract feature information of the high-level convolution to generate compact weld features.The accuracy of the results tested on the collected data reached 99.80%,and the mean intersection over union reached 82.67%.The network has few parameters(only 22.58 MB)and has achieved better results compared with traditional methods.Therefore,the network model built in this thesis has high detection accuracy and strong anti-interference ability,which can satisfy the requirements of detecting weld laser fringes in automatic arc welding.
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