5results about How to "Fast classification" patented technology

The invention discloses a financial bill intelligent classification method and device based on artificial intelligence. The method comprises the following steps: receiving a financial bill image; inputting the financial bill image into a pre-trained convolutional neural network model, and outputting a financial bill category; the convolutional neural network model comprises a residual feature encoder and a classifier; the residual error feature encoder is used for extracting a layout topological structure and a landmark pattern of the financial bill image; the classifier is used for determining the type of the financial bill by using the layout topological structure and the landmark pattern; and the landmark pattern reflects the category and authenticity of the financial bill. According to the invention, the accuracy, robustness and efficiency of financial bill distinguishing can be improved.

The invention relates to a crop classification method and system based on coded aperture snapshot calculation spectral imaging, and the method comprises the following steps: S1, capturing an agricultural scene through an optical encoder and an imaging element based on compressed sensing, and obtaining an output result of the optical encoder; s2, constructing a data set of an output result of the optical encoder and a real label of the crop variety, designing a deep learning decoder, constructing a deep learning framework, respectively carrying out training, testing and verification, and finally obtaining a weight file of the model through gradient descent; and S3, collecting compressed data through an optical encoder, and obtaining a crop classification result of an agricultural scene and a hyperspectral image of the corresponding scene in real time through network reasoning by using the obtained weight file. According to the crop classification method and system, an integrated method of optical hardware coding and decoding based on the deep learning network is used for crop classification, the classification speed is greatly improved, and the data storage cost is reduced.

This invention proposes a wafer surface defect classification method based on deep learning networks. This method can quickly and accurately identify wafer surface defect patterns and pinpoint their causes. The method aims to address the problems of high manual labor intensity and low detection efficiency in traditional wafer defect detection methods. The specific process includes: constructing and training a wafer defect detection and classification model based on the WM-811K dataset. The defect detection model is a binary classification model used to determine the presence of defects in the wafer image, while the classification model is a multi-class model used to determine the specific defect pattern category. The sample to be tested is input into the trained wafer defect detection and classification model to determine its defect pattern. Finally, by analyzing the causes of defect patterns in known samples, the causes of defect patterns in the sample to be tested are inferred, continuously optimizing the process flow and improving wafer product yield.

This invention relates to a computer-implemented method for automatically characterizing small bowel lesions in capsule endoscopy images, comprising detecting small bowel lesions in medical images by using a convolutional image feature extraction step, followed by an architecture that classifies pixels as lesions or non-lesions into one or more categories.

The application provides an image classification model construction method and device. A training data set can include multiple training images, the training images have training labels, and each classification node of an image classification model can be constructed based on the training data set, and the classification node has a shunt rule. In the process of constructing each classification node, if a target node in each classification node corresponds to a defect image corresponding to multiple training labels, multiple child nodes of the target node are constructed, that is, the image classification model is a tree structure, the number of classification nodes is determined according to the training process, and the parameter amount is relatively low, so the classification speed is faster. The target node is used for classifying the defect image corresponding to the target node according to the shunt rule of the target node, obtaining multiple groups of defect images corresponding to the multiple child nodes one by one, so that the defect image can be classified by each classification node in turn, so that the defect category of the defect image can be accurately and quickly determined, and the classification accuracy and speed are improved.