5results about How to "Reduce redundant features" patented technology

The application is suitable for the technical field of computer application, and provides a two-dimensional code image feature extraction method and device based on a CNN-MALT hybrid architecture, which comprises the following steps: inputting a target two-dimensional code image into a CNN preprocessing module of a preset lightweight image feature extraction network to generate first structure perception features; inputting the first structure perception features into a MALT encoder of the preset lightweight image feature extraction network, performing feature extraction on the first structure perception features based on a proxy attention mechanism to generate deep features; and inputting the deep features into a feature fusion module of the preset lightweight image feature extraction network to generate target features. Thus, the structure features of the two-dimensional code image are perceived by the CNN preprocessing module, the deep features are generated by the MALT encoder based on the proxy attention mechanism, and the deep features are fused by the feature fusion module, so as to ensure that the calculation complexity is reduced while the two-dimensional code image feature extraction accuracy is improved.

The invention discloses a DRF-Net-based small target detection method under a view angle of an unmanned aerial vehicle. Constructing an unmanned aerial vehicle aerial photography small target detection data set; a frequency domain guide feature enhancement module FCSA is introduced in the shallow layer stage of the backbone network; a multi-frequency reconstruction module MFRB is introduced in the high-level stage; constructing DRF-Neck in a neck structure, generating a saliency graph S based on MFRB output features, and introducing saliency-driven deformable convolution SAR-DCN, content aware recombination S-CARAFE and a dynamic feedback controller DFC to form a closed-loop optimization mechanism of saliency generation-self-routing convolution-dynamic feedback; and finally, the features are input into a decoupling detection head, network parameters are optimized through joint optimization of cross entropy loss and AHIoU loss, and a small target detection result in an unmanned aerial vehicle aerial photographing scene is output by using non-maximum suppression in a reasoning stage. Compared with the prior art, the method has the advantages that the detection precision on the unmanned aerial vehicle aerial small target data set is improved, and the method can be applied to the fields of unmanned aerial vehicle inspection, urban security monitoring, traffic target detection and the like.

This invention discloses a method, apparatus, and medium for predicting lymph node metastasis. The method involves acquiring a three-dimensional image of a lymph node to be detected and determining the region of interest (ROI) within the image. Feature extraction is performed on the ROI to obtain local features corresponding to the three-dimensional lymph node image. The ROI data is then expanded to obtain pixel blocks to be processed, and feature extraction is performed on these pixel blocks to obtain global features corresponding to the three-dimensional lymph node image. Based on the local and global features, the lymph node metastasis prediction result for the three-dimensional lymph node image is determined. This invention achieves a comprehensive description of the features of the three-dimensional lymph node image through the complementarity of local and global features, solving the problem of low accuracy in lymph node metastasis prediction and improving its reliability. Furthermore, the local features only include the features of the lymph node itself, reducing redundant features.

This invention discloses a lightweight asphalt road defect detection method and system with partial multi-scale attention, belonging to the field of computer vision and intelligent transportation technology. This invention is applicable to the real-time detection and precise localization of defects such as cracks and potholes. It employs PMA modules, SCConv modules, and the inner-AIoU loss function to construct the required asphalt road detection model, achieving accurate detection of the road image to be detected. The model constructed in this invention, while maintaining its lightweight advantage, achieves significant improvements in speed, substantial increases in defect detection accuracy, and diversification of detected defect categories, fully meeting the needs of real-time detection. Furthermore, the detection model constructed in this invention also possesses good portability and scalability, making its integration with hardware devices more convenient.

The application discloses a bearing steel grinding burn identification method based on multiple magnetic parameters, and relates to the technical field of nondestructive testing.The method first constructs a training set and a test set containing multiple samples, and uses an X-ray stress meter to calibrate the burn state of the training set samples to obtain training set burn samples; the electromagnetic parameters of the training set burn samples are acquired through a micro-magnetic multi-parameter detector, and a judgment index feature based on a physical model is extracted; subsequently, the features are reduced in dimension by using a stacked auto-encoder, and the low-dimensional features are clustered by using a K-means clustering algorithm to automatically calibrate the state of the training set samples and determine a burn judgment threshold; finally, for the test set samples, whether burn occurs is judged by comparing the low-dimensional feature vectors thereof with the threshold value.The application can nondestructively and quickly acquire magnetic characteristic signals, effectively detect grinding burn and reduce misjudgment, and has the advantages of fast detection speed and high precision.