Abstract: In order to realize the intellectualization and automation of welding and assembling technology for high-speed white body,the problems of small feature area and multi-background interference in welding process are solved,a novel fast recognition algorithm of welding assembly based on migration learning and convolution neural network is proposed.Firstly,the traditional image processing algorithms such as binarization are used to determine the rough position of the feature to be extracted.On this basis,Sobel,corrosion and Hough line detection are used to determine the precise position of the feature area.Secondly,considering the different performance of feature regions in different environments,a classification model based on convolution neural network is adopted to enhance the robustness and accuracy of the prediction model.At last,Visual Geometry Group Network (VGG16) based on transfer learning is selected to solve the problem that the number of the samples is not enough to train the parameters of the whole model.The experimental results show that the recognition algorithm proposed in this paper can accurately identify the state of profile,and the detection speed is better than YOLOV3,and the accuracy is inferior to YOLOV3.The algorithm can meet the real-time requirements in the use scene.

Key words: Convolution neural network (CNN), Fast feature recognition, Hough line segment detection, Transfer learning, Visual geometry group network(VGG16)