Wuyi University

Cuilin Yu

Yikui Zhai

Junying Gan

Junying Zeng

Wenbo Deng

Qirui Ke

Zilu Ying

Yanyang Liang

Ying Xu

Tianlei Wang

Chuanbo Qin

Chaoyun Mai

Yihang Zhi

Wenlue Zhou

Zhongxin Yu

Wenlve Zhou

Zhiyong Hong

A double attention network (DANet)-based drone patrol and inspection system for coastline floating garbage, including: an image acquisition module configured to shoot a video of a coastline in need of patrol and inspection by using a drone, and obtain an image from the video; a feature extraction module configured to extract shallow features and deep features, fuse the shallow features and the deep features to obtain a shared feature, and finally output a panoramic recognition result; a network training module configured to perform training on the labeled image so that the network can recognize the coastline and floating garbage; and a path correction module configured to adjust a flying direction of the drone.

DANet-based drone patrol and inspection system for coastline floating garbage

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.

Method and apparatus for predicting face beauty grade, and storage medium

Disclosed are method and apparatus for SAR image recognition based on multi-scale features and broad learning. A region of interest of an original SAR image is extracted by centroid localization, the image is rotated and added with noise for enhancing the data volume, the image is downsampled, LBP features and PPQ features are extracted, an LBP feature vector Xand an LPQ feature vector Xare cascaded to achieve dimension reduction by principal component analysis to obtain a fusion feature data X, the fusion feature data Xis input to a broad learning network for image recognition and a recognition result is output. By fusing the LBP features and the LPQ features, complementary information is fully utilized and redundant information is reduced. The broad learning network is used to improve the training speed and reduce the time cost. As a result, the recognition effect is more stable, robust and reliable.

Method and apparatus for SAR image recognition based on multi-scale features and broad learning

Growing community of inventors

Jiangmen, China

Cuilin Yu