The invention discloses an image classification method based on a convolution neural network. The method comprises the following steps: constructing a deep convolution neural network; improving the deep convolution neural network; training and testing the deep convolution neural network; and optimizing the network parameter. By using the image classification method disclosed by the invention, the improvement and the optimization are respectively performed on the network structure and multiple parameters of the convolution neural network, the recognition rate of the deep convolution neural network can be effectively improved, and the accuracy of the image classification is improved.

Image classification method based on convolution neural network

The invention provides a text semantic understanding method, device and system The method comprises the steps that text pending to be subjected to semantic understanding is received; the text pending to be subjected to semantic understanding is subjected to semantic understanding according to a deep residual network model constructed in advance, and a semantic understanding result is obtained By means of the method, the text semantic understanding effect can be improved

Text semantic understanding method, device and system

The invention discloses a gesture identification method based on a deep residual error network The gesture identification method based on a deep residual error network includes the steps: 1) acquiring the original data information of gesture, and marking N points to obtain the 2N-dimensional original tag data; 2) preprocessing the original data information and the 2N-dimensional tag data; 3) taking the preprocessed original data information and the 2N-dimensional tag data converted in the hdf5 format as the original training data, inputting the original training data to a deep residual error network to train network parameters, and obtaining a gesture identification model; 4) performing N-point marking being identical to the step 1 on the gesture to be identified to obtain the 2N-dimensional tag data to be identified; and 5) preprocessing the gesture data to be identified and the 2N-dimensional tag data to be identified, inputting the processed gesture data and the 2N-dimensional tag data to be identified converted in the hdf5 format to the gesture identification model for identification, and then obtaining an identification result The gesture identification method based on a deep residual error network effectively solves the gradient dispersion problem and the network accuracy problem

Gesture identification method based on deep residual error network

The invention discloses a depth residual network construction method for mobile phone screen defect detection. The method comprises steps: 1) pictures with defects and normal pictures are acquired respectively, marking is carried out (1 presents a picture with a defect, and 0 presents a normal pictu re), and a user-defined depth residual network is trained through training data until convergence and high accuracy; 2) a method of using a certain probability for random removal for each residual module in the depth residual network, a shallow network model is generated, the operation is repeated, and multiple network models with different depths are generated; 3) a mobile phone screen picture photographed by a high-resolution camera is subjected to scaling in different proportions, a picture pyramid is formed, as for a picture of each scale, the picture is segmented to small blocks, the picture block contains a certain overlapped area, and all block pictures serve as one group to be together fed to the network models with different depths; and 4) a feature graph outputted by each network model is selected as a defect response graph, a threshold segmentation method is adopted to acquire the mobile phone screen defect area position, detection results of the network models with different depths are overlapped finally, and a final detection result is thus acquired.

Depth residual network construction method for mobile phone screen defect detection

A MODIS fog monitoring method based on a deep belief network combines a deep belief network with MODIS remote sensing data, and reduces the difficulty of subsequent training of the deep network and improves the training accuracy by means of pre-training layer by layer at a restrained Boltzmann machine. In addition, a random dropout optimization method is added, and the generalization ability of the model is enhanced, so that a more accurate fog monitoring model is obtained and the monitoring accuracy is improved.

MODIS fog monitoring method based on deep belief network

The invention relates to a hyperspectral haze monitoring method based on a depth residual error network According to the hyperspectral haze monitoring device, the depth residual error network is combined with hyperspectral remote sensing data, and a shortcut passage method is added in the network, so that the training difficulty is lowered, the training precision is improved, a relatively accurate haze monitoring model is obtained, and the monitoring precision is improved

Hyperspectral haze monitoring method based on depth residual error network

A multi-source aerial photo-based abnormal scene identification method is disclosed. A temperature anomaly area set and a strong dielectricity area set are detected in far-infrared and SAR images, and the temperature anomaly area set and the strong dielectricity area set are combined into a suspected anomaly area set. Three source (visible light, far-infrared and SAR) features of the suspected anomaly area set are extracted one by one, and the three source features are fused on a feature level. At last, a fused feature is input into a classifier for classification identification. According to the multi-source aerial photo-based abnormal scene identification method, temperature anomaly and presence of artificial metal products are taken as common attributes of abnormal scenes, a feasible and practical identification framework is provided, and a research gap of abnormal scene identification based on three source images is filled.

Multi-source aerial photo-based abnormal scene identification method

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Hyperspectral haze monitoring method based on depth residual error network

Multi-source aerial photo-based abnormal scene identification method