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Detection methods for interplanetary coronal mass ejections (ICMEs) from in situ spacecraft measurements are mostly manual, which are labor-intensive and time-consuming, being prone to the inconsistencies of identification criteria and the incompleteness of the existing catalogs. Therefore, the automatic detection of ICMEs has aroused the interest of the astrophysical community. Of these automatic methods, the convolutional neural network–based methods show the advantages of fast speed and high precision. To further improve the computing feasibility and detection performance, this paper proposes a method called residual U-net (RU-net), from the perspective of time-series segmentation. With the help of U-net architecture, we design an encoder–decoder network with skip connection to capture multiscale information, where the end-to-end architecture with an embedded residual element is formulated to accelerate the algorithmic convergence. For the in situ data from 1997 October 1 to 2016 January 1 collected by the Wind spacecraft, the results of our experiments demonstrate the competitive performance of the proposed RU-net in terms of accuracy and efficiency (178 of 230 ICMEs are detected in the test set, and the F1 score is 80.18%).

Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.