Abstract: A method for predicting a face beauty grade includes the following steps of: acquiring a beautiful face image of a face beauty database, preprocessing the beautiful face image, and extracting a beauty feature vector of the beautiful face image, the preprocessing unifying data of the beautiful face image; recognizing continuous features of samples of the same type in a feature space by using a bionic pattern recognition model, and classifying the beauty feature vector to obtain a face beauty grade prediction model; and collecting a face image to be recognized, and inputting the face image to be recognized into the face beauty grade prediction model to predict a face beauty grade and obtain the beauty grade of the face image to be recognized.

Abstract: A method and system for mobile communication base station antenna measurement is disclosed. The method comprises steps of: acquiring a set of images containing antennas of a base station; processing the set of images with a model based on instance segmentation network, and generating visualized images corresponding to the set of images of antennas; calculating, from the visualized images, the quantity of antennas of the base station and separating data for each antenna; measuring parameters of each antenna by data fitting. The system comprises a processor and a memory storing program instructions thereon, the program instructions executable by the processor to cause the system to perform the steps of the method.

Abstract: Disclosed are a method and an apparatus for multi-scale SAR image recognition based on attention mechanism. According to the method, a whole image recognition network is adjusted by training a SAR training image by an attention prediction subnet, a region-of-interest positioning subnet and an image classification subnet in combination with a network loss, which greatly improves a network performance; and in addition, an attention prediction map is generated by attention mechanism to position a most prominent feature part in the SAR image, which greatly eliminates a redundancy of image features in a machine vision, effectively determines a region-of-interest, reduces interference of image noises, greatly reduces an image processing time, improves a target recognition accuracy, is beneficial to next target positioning, and has a significant improvement on a network recognition speed integrally.

Abstract: A method and system for mobile communication base station antenna measurement is disclosed. The method comprises steps of: acquiring a set of images containing antennas of a base station; processing the set of images with a model based on instance segmentation network, and generating visualized images corresponding to the set of images of antennas; calculating, from the visualized images, the quantity of antennas of the base station and separating data for each antenna; measuring parameters of each antenna by data fitting. The system comprises a processor and a memory storing program instructions thereon, the program instructions executable by the processor to cause the system to perform the steps of the method.

Abstract: Disclosed are a method and an apparatus for multi-scale SAR image recognition based on attention mechanism. According to the method, a whole image recognition network is adjusted by training a SAR training image by an attention prediction subnet, a region-of-interest positioning subnet and an image classification subnet in combination with a network loss, which greatly improves a network performance; and in addition, an attention prediction map is generated by attention mechanism to position a most prominent feature part in the SAR image, which greatly eliminates a redundancy of image features in a machine vision, effectively determines a region-of-interest, reduces interference of image noises, greatly reduces an image processing time, improves a target recognition accuracy, is beneficial to next target positioning, and has a significant improvement on a network recognition speed integrally.

Abstract: A method for predicting a face beauty grade includes the following steps of: acquiring a beautiful face image of a face beauty database, preprocessing the beautiful face image, and extracting a beauty feature vector of the beautiful face image, the preprocessing unifying data of the beautiful face image; recognizing continuous features of samples of the same type in a feature space by using a bionic pattern recognition model, and classifying the beauty feature vector to obtain a face beauty grade prediction model; and collecting a face image to be recognized, and inputting the face image to be recognized into the face beauty grade prediction model to predict a face beauty grade and obtain the beauty grade of the face image to be recognized.