KEYWORDS: Point clouds, Feature extraction, Matrices, Computer vision technology, Deep learning, Singular value decomposition, Education and training, Data modeling, Detection and tracking algorithms, Adaptive optics
In the field of computer vision such as target detection and 3D positioning, point cloud registration has always been one of the key problems, which requires alignment two point clouds through rigid spatial transformation, accuracy, robustness, speed and other factors. Point cloud registration based on deep learning has received a lot of research in recent years. Compared with the conventional methods, They show a great advantage in their registration performance, To improve the performance of deep learning on point-cloud registration, This paper uses Kernel correlation to compute and store the neighborhood information of point clouds, While extracting local geometric features by convolutional neural network and Offset_Attention module aggregation, Then use Singular value decomposition SVD to predict the final rigid transformation matrix, and finally achieve high-quality registration. In this paper, by training our model on the ModelNet40 dataset, And the source and target point cloud are sampled independently, And also extract the non-axisymmetric targets for additional tests, Achievalized a more equitable registration network experiment, the Root-mean-square deviation (RMSE) is 12.58% higher than before, which verifies the effectiveness of our network.
The defect of the train wheel tread is a threat to its safe driving, and the defect detection of the tread is an important work. The extraction of defect area is a crucial link. In this paper, we propose a segmentation algorithm of tread defect area based on attention mechanism, which realizes the more accurate segmentation of tread defect area.This algorithm uses U-net as the backbone network, firstly, introduces the Lovasz-Softmax loss, secondly, CBAM is introduced between the encoder and decoder. Get the attention feature map information in the channel and space dimensions, and then multiply the two feature map information with the original input feature map to make adaptive feature correction to obtain a more accurate feature map and improve the accuracy of the segmentation algorithm.Validated on the dataset of train wheel tread, and the experimental results show that the algorithm PA is 99.54% and mIoU is 98.27%, which improves by 0.83% and 0.73% compared with Unet algorithm, which verifies the effectiveness of the algorithm.
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