基于双目视觉与Transformer的连铸坯模型定位与测量OA北大核心CSTPCD

In order to address the problems of low efficiency and complex matching of traditional binocular vision detection algorithms,a continuous casting slab model positioning and measurement based on binocular vision and Transformer method was proposed in this paper.Firstly,a calibrated parallel binocular camera was used to collect images of the continuous casting slab model,which were used as datasets after correction and labeling.Then,with the proposed Transunet* as the backbone,a neural network was used to output the key point coordinates of the datasets.The network model adopted a multi-scale U-shape structure to offset the lower bound of theoretical error of Gaussian heatmap caused by the downsampling quantization.In order to improve the defect that convolutional neural networks only focus on local features,Transformer module was added to enhance the information exchange in each channel,and an optimized loss function calculation method was proposed to overcome the problem of the misproportion of positive and negative samples and accelerate network convergence.Finally,the network output was reconstructed with binocular vision to complete the distance measurement.The results show that the proposed algorithm outperforms other neural network methods in the detection accuracy of key points.Compared with the sub-optimal methods,the root-mean-square error and normalized mean error the proposed method are reduced by 17.24%and 18.58%,respectively.In the three-dimensional ranging,the accuracy of the proposed method is obviously superior to that of the traditional feature detection algorithm.Thus,the proposed method can meet the requirements of high precision and small environmental impact in industrial measurement and positioning.